Development and validation of risk prediction models for permanent hypocalcemia after total thyroidectomy in patients with papillary thyroid carcinoma

Development and validation of risk prediction models for permanent hypocalcemia after total thyroidectomy in patients with papillary thyroid carcinoma | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Development and validation of risk prediction models for permanent hypocalcemia after total thyroidectomy in patients with papillary thyroid carcinoma BoHan Cao, CanGang Zhang, MingMing Jiang, Yi Yang, XiCai Liu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4774077/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 18 Mar, 2025 Read the published version in Scientific Reports → Version 1 posted 11 You are reading this latest preprint version Abstract Hypocalcemia is a common complication and can be permanent in patients following total thyroidectomy (TT). The aim of this study was to identify factors associated with permanent hypocalcemia and to develop a validated risk prediction model for permanent hypocalcemia to assist surgeons in the appropriate follow-up of high-risk patients regarding supplemental therapy. We included data of 92 patients with papillary thyroid carcinoma (PTC) undergoing TT who were randomly allocated in a 7:3 ratio to a training set (n = 65) and validation set (n = 27). Univariate and multivariate logistic regression analyses revealed significant correlations of permanent hypocalcemia with parathyroid hormone (PTH) at postoperative month 1 (IM PTH), IM calcium (Ca), and IM phosphorus (P). These variables were constructed two models. Model 1 used the three indicators listed above; model 2 also included tumor, node, metastasis staging. The receiver operating characteristic (ROC) curve analysis showed that the areas under the curve (AUC) for models 1 and 2 were high for both the training set (0.905/0.913) and the validation set (0.894/0.800). Calibration curves showed good agreement between the incidence of permanent hypocalcemia estimated using the predictive models and the actual incidence. Model 1 may be more concise and convenient for clinical use. Health sciences/Endocrinology Health sciences/Risk factors Predictive model Nomogram Papillary thyroid carcinoma Total thyroidectomy Hypocalcemia Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Introduction With the increasing popularity of neck ultrasound, the detection and incidence of thyroid cancer is gradually increasing. According to the World Health Organization (WHO) International Agency for Research on Cancer, the incidence of thyroid cancer is the ninth highest in the world, and it is more common in women, at approximately 75%. The median age of patients with thyroid cancer is approximately 50 years, but thyroid cancer is also the most common malignant tumor in people aged 16 to 33 years. PTC accounts for approximately 80% of patients with thyroid malignancy. Surgery remains the primary treatment option for patients with a high suspicion and confirmed PTC by puncture cytology, and TT is the preferred treatment option for non-low risk patients 1 . As with other surgical procedures, patients undergoing TT may experience postoperative complications. Of these, hypocalcemia has a considerable impact on patients' quality of life in the postoperative period. In 2016, the American Thyroid Association defined hypocalcemia following TT as transient if occurring within 6 months of surgery and permanent if lasting for 6 months or longer postoperatively. Hypoparathyroidism (HPT) is defined as hypocalcemia and inadequate PTH levels, and is diagnosed as a permanent HPT if it persists for 6 or more months after surgery 2 . Most current research on permanent hypocalcemia and HPT recognizes 6 months postoperatively as the time point when the greatest changes occur 3 – 8 . Hypocalcemia can cause a number of adverse reactions in patients, including muscle twitching, cramps, tingling and numbness, as well as spasms of the facial muscles and wrists. In severe cases, hypocalcemia may induce epilepsy or even cardiac arrhythmias. It is also possible for patients with hypocalcemia to present with no obvious symptoms, although this may be associated with the development of adverse psychiatric symptoms 9 . The early detection of postoperative hypocalcemia and prediction of permanent hypocalcemia are of great importance to clinicians. Moreover, timely intervention will minimize the adverse effects on patients 10 . Nomograms are a common tool for assessing the prognosis of oncological diseases 11 , 12 . A nomogram is a pictorial representation of a complex mathematical formula. Nomograms use biological and clinical variables to graphically depict a statistical prognostic model that generates a probability of a clinical event for a given individual 13 , 14 . Also, nomograms can be used to predict the risk of postoperative complications 15 , 16 . In the context of hypocalcemia following TT, most previous studies have concentrated on the examination of risk factors and their predictive value. However, there have been few attempts to develop risk prediction models for the screening of patients at high risk of developing permanent hypocalcemia after TT. The aim of this study was to identify factors associated with permanent hypocalcemia and their follow-up after TT and incorporate these into a nomogram constructed based on a model for predicting permanent postoperative hypocalcemia. Materials and methods Study population In total, 106 patients with malignant thyroid tumors who underwent TT between August 2021 and March 2023 were retrospectively identified in a search of the electronic medical records at Benxi Central Hospital of China Medical University. After 14 exclusions (one patient each with follicular thyroid carcinoma, medullary thyroid carcinoma, preoperative hypocalcemia, and combined hyperthyroidism; four patients with other diseases affecting postoperative Ca in the serum ; and six patients who were lost to follow-up after surgery), data for 92 patients with PTC were included in the statistical analysis. Ethics approval The study was approved by the Ethics Committee of Benxi Central Hospital of China Medical University. This study adhered to the tenets of the Declaration of Helsinki. This was a retrospective study, and the data used were obtained from the hospital's electronic medical records, with patient identifiers removed, so that patient privacy and related data would not be disclosed. The study was approved by the ethics committee of our institution, and informed consent was exempted. Surgical procedures All patients underwent open surgery performed by the same surgical team using an anterior cervical approach with an ultrasonic scalpel. According to the Chinese and American guidelines for the diagnosis and treatment of differentiated thyroid cancer, TT includes central lymph node dissection (CLND) on at least one side of the thyroid gland with the upper border reaching the hyoid bone, the lower border flat on the plane of the innominate artery, the common carotid artery on the outer border, the more superficial deep cervical fascia on the anterior border, and the deeper deep cervical fascia on the posterior border. Lateral cervical lymph node dissection (LCLND) was performed if the lateral cervical region showed evidence of metastases. The lateral cervical region was routinely dissected in zones II to V, up to the digastric muscle, down to the superior margin of the clavicle, inward to the medial border of the carotid sheath, and outward to the anterior border of the trapezius muscle 17 , 18 . Eligibility criteria The following inclusion criteria were applied: TT for the first time and postoperative pathological confirmation of PTC; pathology corresponding to the American Joint Committee on Cancer tumor, node, metastasis (TNM) stage; preoperative serum PTH, Ca, P, and magnesium (Mg) levels recorded and rechecked during follow-up at the same hospital; and complete medical and surgical records. Patients with any of the following were excluded: hepatic or renal insufficiency; hyperthyroidism and/or toxic diffuse goiter; preoperative parathyroid or other endocrine disorder; pregnancy and/or lactation; important follow-up data missing; or a disorder of Ca, P, or Mg metabolism before surgery or during follow-up. Indicators and definitions The general data collected from patients included information on their age, sex, height, weight, and whether they had any underlying comorbidities associated with Hashimoto's thyroiditis (HT). Additionally, the surgical procedure and pathological staging were documented. Laboratory investigation data encompassed the patients' preoperative Ca, P, Mg, and PTH levels (formulated as the Pre + index), postoperative day 1 (PODI )(formulated as the PODI + index), and postoperative Ca, P, Mg, and PTH levels at 1, 3, and 6 months postoperatively. All blood samples were taken in the morning after an overnight fast. Defining indicators were as follows: change in PTH=(Pre PTH − PODI PTH)/Pre PTH0; change in serum Ca=(Pre Ca − PODI Ca)/Ca0; change in serum Mg=(Pre Mg − PODI Mg)/Pre Mg0; change in serum P=(PODI P − Pre P)/Pre P0. Study groups Included patients were divided into a permanent hypocalcemia group and a non-hypocalcemia group based on a serum Ca threshold of 2.10 mmol/L at 6 months postoperatively. Statistical analysis Measurements are expressed as mean ± standard deviation. Counts are expressed as number (percentage). The data were randomly divided into a training set (n = 65) and a validation set (n = 27) according to a 7:3 ratio. Column line plots were used to illustrate the risk of permanent hypocalcemia in patients following TT. Multivariate logistic regression analyses were conducted to develop and validate the models. Initially, all data from the training and validation sets were analyzed to ascertain whether there was a statistically significant difference between the two sets. This was followed by an analysis of the training sets using univariate logistic regression to identify predictors of permanent hypocalcemia in patients following TT. Finally, the selected predictors were included in multifactorial logistic regression analyses and incorporated into nomograms. The predictive models were validated using discrimination, accuracy, and clinical validity. In this study, ROC curve analysis showed that AUC could be used to determine the discriminative power of the model.Calibration curves were used to determine the degree of agreement between predicted probabilities and observed outcomes. To assess the clinical validity of the model, decision curve analysis (DCA) was applied. The “pROC” package in R was used to plot ROC curves and the “RMDA” package was used to plot DCA curves. The “RMS” package was used to plot calibration curves and nomograms. All data analyses were conducted using R software version 4.3.0 (The R Project for Statistical Computing, Vienna, Austria), and P-values < 0.05 were considered statistically significant. Results Participant characteristics The baseline characteristics of all patients as well as patients in the training and validation sets were assessed, and the training and validation sets were analyzed. No significant discrepancies were observed in the data between the training set and validation set (Table 1 ). Table 1 Comparison between variables in the training and validation sets. P < 0.05 indicates statistical significance. BMI, body mass index; HT, Hashimoto's thyroiditis; PTH, parathyroid hormone; PODI, postoperative day 1; Ca, calcium; Mg, magnesium; P, potassium; CLND, central lymph node dissection; LCLND, lateral cervical lymph node dissection; T, tumor; N, node; M, metastasis; IM, postoperative month 1; IIIM, postoperative month 3; VIM, postoperative month 6; BCLND, bilateral cervical lymph node dissection. Variables Total Training set Validation set P- Value n = 92 n = 65 n = 27 Permanent Hypocalcemia(%) 40(43.5%) 30(46.2%) 10(37.0%) 0.567 Age(years) 49.80 ± 9.88 48.80 ± 9.99 52.15 ± 9.37 0.132 Gender 0.447 Female(%) 72(78.3%) 49(75.4%) 23(85.2%) Male(%) 20(21.7%) 16(24.6%) 4(14.8%) Height(cm) 163.89 ± 7.67 164.18 ± 8.02 163.19 ± 6.85 0.688 Weight(kg) 68.41 ± 12.62 69.32 ± 13.72 66.22 ± 9.34 0.216 BMI(kg/ m 2 ) 25.36 ± 3.61 25.56 ± 3.74 24.88 ± 3.29 0.391 HT(%) 29(33.8%) 22(33.8%) 7 (25.9%) 0.618 Pre PTH(pg/mL) 63.66 ± 21.58 61.83 ± 18.74 68.05 ± 27.13 0.425 Pre Ca(mmol/L) 2.29 ± 0.10 2.29 ± 0.11 2.29 ± 0.10 0.817 Pre Mg(mmol/L) 0.92 ± 0.07 0.91 ± 0.07 0.94 ± 0.07 0.091 Pre P(mmol/L) 1.17 ± 0.18 1.19 ± 0.19 1.13 ± 0.14 0.315 PODI PTH(pg/mL) 22.90 ± 17.34 21.68 ± 17.18 25.84 ± 17.7 0.245 PODI Ca(mmol/L) 2.05 ± 0.16 2.05 ± 0.15 2.06 ± 0.19 0.955 PODI Mg(mmol/L) 0.82 ± 0.07 0.81 ± 0.07 0.83 ± 0.08 0.251 PODI P(mmol/L) 1.32 ± 0.25 1.32 ± 0.25 1.32 ± 0.27 0.784 PTH change 0.64 ± 0.26 0.65 ± 0.25 0.59 ± 0.29 0.443 Ca change 0.10 ± 0.07 0.10 ± 0.07 0.1 ± 0.09 0.859 Mg change 0.11 ± 0.08 0.11 ± 0.08 0.11 ± 0.08 0.849 P change 0.04 ± 0.29 0.03 ± 0.26 0.05 ± 0.35 0.786 IM PTH(pg/mL) 51.69 ± 20.97 49.27 ± 19.76 57.51 ± 22.96 0.110 IM Ca(mmol/L) 2.18 ± 0.18 2.18 ± 0.19 2.21 ± 0.16 0.461 IM Mg(mmol/L) 0.90 ± 0.07 0.91 ± 0.08 0.9 ± 0.06 0.458 IM P(mmol/L) 1.26 ± 0.20 1.26 ± 0.20 1.24 ± 0.21 0.722 IIIM PTH(pg/mL) 54.94 ± 37.49 53.76 ± 41.17 57.77 ± 27.09 0.233 IIIM Ca(mmol/L) 2.17 ± 0.16 2.16 ± 0.16 2.20 ± 0.16 0.352 IIIM Mg(mmol/L) 0.90 ± 0.06 0.89 ± 0.06 0.90 ± 0.06 0.478 IIIM P(mmol/L) 1.23 ± 0.27 1.23 ± 0.29 1.22 ± 0.23 0.767 VIM PTH(pg/mL) 46.94 ± 19.92 45.75 ± 18.75 49.82 ± 22.63 0.414 VIM Ca(mmol/L) 2.13 ± 0.18 2.12 ± 0.18 2.14 ± 0.18 0.352 VIM Mg(mmol/L) 0.89 ± 0.07 0.89 ± 0.07 0.89 ± 0.07 0.867 VIM P(mmol/L) 1.17 ± 0.21 1.17 ± 0.21 1.17 ± 0.20 0.938 Surgical procedure 0.307 BCLND(%) 72(78.3%) 53(81.5%) 19(70.4%) Left CLND(%) 3(3.3%) 3(4.6%) 0(0%) Right CLND(%) 5(5.4%) 3(4.6%) 2(7.4%) BCLND + Left LCLND(%) 7(7.6%) 3(4.6%) 4(14.8%) BCLND + Right LCLND(%) 5(5.4%) 3(4.6%) 2(7.4%) Pathological staging T 0.288 TIa(%) 58(63.1%) 41(63.1%) 17(63%) TIb (%) 23(25.0%) 18(27.7%) 5(18.5%) TII (%) 4(4.3%) 1(1.5%) 3(11.1%) TIIIb (%) 6(6.5%) 4(6.2%) 2(7.4%) TIVa (%) 1(1.1%) 1(1.5%) 0(0%) N 0.412 N0(%) 65(70.7%) 45(69.2%) 20(74.1%) NIa (%) 23(25.0%) 18(27.7%) 5(18.5%) NIb (%) 4(4.3%) 2(3.1%) 2(7.4%) M0(%) 92(100%) 65(100%) 27(100%) - Results of univariate and multivariate analyses Univariate and multivariate logistic regression analyses were performed to identify risk factors for permanent hypocalcemia in 65 patients after TT. The parameters for which P < 0.20 was calculated in univariate logistic regression analysis (PODI PTH, PODI Ca, PODI P, PTH change, Ca change, P change, postoperative month 1 [IM] PTH, IM Ca, IM Mg, IM P, postoperative month 3 [IIIM] PTH, IIIMCa and T stage) were included in multivariate logistic regression analysis. The results of this analysis identified IM PTH, IM Ca, and IM P (P < 0.05) as independent risk factors for permanent hypocalcemia in patients with PTC after TT (Table 2 ). Table 2 Univariate and multivariate logistic regression analyses. BMI, body mass index; HT, Hashimoto's thyroiditis; PTH, parathyroid hormone; PODI, postoperative day 1; Ca, calcium; Mg, magnesium; P, potassium; CLND, central lymph node dissection; LCLND, lateral cervical lymph node dissection; T, tumor; N, node; M, metastasis; IM, postoperative month 1; IIIM, postoperative month 3; OR, odds ratio; CI, confidence interval; VIM, postoperative month 6; BCLND,bilateral cervical lymph node dissection. variables Univariate analyse Multivariate analyse OR 95% CI P-Value OR 95% CI P Value Age 1.002 0.953–1.053 0.940 Gender 1.227 0.392–3.857 0.723 Height(cm) 1.021 0.960–1.088 0.503 Weight(kg) 0.995 0.959–1.031 0.778 BMI(kg/ m 2 ) 0.936 0.814–1.069 0.338 HT 1.667 0.593–4.778 0.333 Pre PTH(pg/mL) 1.005 0.978–1.032 0.731 Pre Ca(mmol/L) 0.061 < 0.001–6.670 0.252 Pre Mg(mmol/L) 6.718 0.006-9339.537 0.597 Pre P(mmol/L) 2.124 0.158–31.519 0.569 PODI PTH(pg/mL) 0.965 0.931–0.996 0.039 0.997 0.827–1.211 0.969 PODI Ca(mmol/L) 0.032 0.001–0.877 0.050 1.345e + 14 8.874-1.643e + 36 0.089 PODI Mg(mmol/L) 2.060 0.002-2529.993 0.838 PODI P(mmol/L) 4.683 0.610-48.892 0.158 5.311 < 0.001-2.296e + 05 0.737 PTH change 27.342 2.988-347.751 0.006 43.196 0.002-1.90e + 07 0.490 Ca change 146.888 0.094-4.236e + 05 0.195 2.590e + 27 3162.804-4.928e + 67 0.070 Mg change 1.779 0.004-936.644 0.855 P change 8.010 1.098–75.526 0.051 217.715 0.460-3.222e + 06 0.150 IM PTH(pg/mL) 0.936 0.900-0.967 < 0.001 0.760 0.581–0.895 0.008 IM Ca(mmol/L) < 0.001 < 0.001–0.012 0.001 3.397e-08 8.512e-17-0.004 0.019 IM Mg(mmol/L) 0.005 < 0.001–3.890 0.130 1.126e + 07 0.139-3.027e + 18 0.123 IM P(mmol/L) 88.497 26.853-69553.910 0.001 1.223e + 07 502.204-1.248e + 15 0.015 IIIM PTH(pg/mL) 0.964 0.936–0.988 0.007 1.022 0.894–1.110 0.740 IIIM Ca(mmol/L) 0.002 2.504e-05-0.069 0.002 0.001 1.356e-09-14.208 0.244 IIIM Mg(mmol/L) 0.419 < 0.001-990.781 0.825 IIIM P(mmol/L) 2.970 0.501–27.713 0.267 Surgical procedure BCLND Reference Reference Reference Left CLND 0.604 0.027–6.681 0.688 Right CLND 0.604 0.027–6.681 0.688 BCLND + Left LCLND 2.417 0.219–53.888 0.482 BCLND + Right LCLND 2.417 0.219–53.888 0.482 Pathological staging T TIa Reference Reference Reference Reference Reference Reference TIb 2.455 0.801–7.962 0.121 4.138 0.091-342.638 0.463 TII 2.446e + 07 1.196e-205-NA 0.994 0.730 2.134e-149-NA 0.100 TIIIb 1.563 0.173–14.117 0.671 0.082 < 0.001–12.176 0.362 TIVa < 0.001 NA-2.029e + 205 0.854 4.081e-09 NA-1.403e + 140 0.996 N N0 Reference Reference Reference NIa 1.563 0.521–4.811 0.427 NIb < 0.001 NA-2.94e + 108 0.788 Predictive model development The prediction model comprised variables with P values < 0.05 in multivariate logistic regression, which were IM PTH, IM Ca and IM P. Predictive model 1 was presented using a nomogram that can be used to quantitatively predict the risk of permanent hypocalcemia in patients after TT. Model 1 was as follows: Logit (p) = 11.9478 − 0.0556×IM PTH − 8.3677×IM Ca + 7.3355×IM P. Model 2 builds on this by incorporating pathological staging and investigating the effect of pathological staging on the predictive model. Model 2 was as follows: Logit (p) = 9.0389 − 0.0556×IM PTH − 6.8710×IM Ca + 6.9819×IM P + 0.7505×TIb + 6.7132×TII + 0.1513×TIIIb − 7.1288×TIVa − 0.5418×NIa − 7.3310×NIb. To increase the usefulness of these models, we generated nomograms showing scores that correspond to each risk factor and a total of all risk factors, corresponding to the risk of permanent hypocalcemia (Figs. 1 and 2 ) The application of the nomograms was as follows. According to the figures, the score value corresponding to each prediction index was found and recorded as the total score. The prediction probability corresponding to the total score in the last row of the figure is the risk of permanent hypocalcemia (range 0–1). Predictive model validation Discrimination AUC values of the ROC curves were calculated to assess the discrimination of the predictive model by examining the occurrence of permanent hypocalcemia in patients following TT in the training and validation sets of model 1 and 2. As shown in Fig. 3 A and 3 B, the predictive model 1 yielded an AUC value of 0.905 (95% CI = 0.828–0.982), with a specificity of 0.914, and sensitivity of 0.767 in the training set, and an AUC = 0.894 (95% CI = 0.777–1.000), with a specificity of 0.706 and sensitivity of 1.00 in the validation set. Predictive model 2 had an AUC value of 0.913 (95% CI = 0.844–0.982), with a specificity of 0.886 and sensitivity of 0.800 in the training set, and AUC = 0.800 (95% CI = 0.630–0.970), with a specificity of 0.706 and sensitivity of 0.900 in the validation set (Fig. 4 A and 4 B). These data indicated that the two nomograms have good discriminatory ability and predictive value and can correctly identify patients at risk of permanent hypocalcemia. Delong test results The Delong test was used to determine statistically significant differences in the ROC curves. The Delong test was used for the validation set of models 1 and 2 (Z = 1.199, 95% confidence interval [CI] = − 0.060–0.248, P = 0.231). The result showed no statistically significant differences between the two ROC curves and the AUC. Model 2 included TNM staging, indicating that whether the model includes TNM staging makes little difference to the predictive value. Thus, model 1 was found to be more concise than model 2, which is more conducive to clinical application. Calibration of the predictive models We constructed calibration curves for the models. The curves for models 1 and 2 in the training set showed that the curve performed well with an additional 1000 bootstraps but performed slightly worse in the validation set (Fig. 5 A and 5 B, 6 A and 6 B) Evaluation of clinical validity The clinical validity of the model was evaluated using DCA; the results are shown in Figs. 7 A, 7 B, 8 A, and 8 B. According to DCA, the net benefits of the predictive models were significantly higher than those of the two extremes. Thus, it could be concluded that the prediction models were clinically effective. Discussion HPT is the most common complication following TT. Biochemical HPT is defined as a low intact PTH level, below the lower limit of the laboratory standard, accompanied by hypocalcemia. Hypocalcemia is a total serum Ca level less than the lower limit of the center-specific reference range 5 , 9 , 19 . Hypocalcemia has the potential to cause adverse effects on both the physiological and psychological well-being of patients. Therefore, it is crucial to identify the risk of developing permanent hypocalcemia in the early postoperative period and during follow-up to implement timely interventions and appropriate psychological treatment and improve the patient's quality of life. A number of studies have been conducted by researchers in various countries on the risk factors of postoperative hypocalcemia following TT. The results of these studies indicate that age, sex, serum Mg, vitamin D, HT, and a low postoperative PTH level may be risk factors for postoperative hypocalcemia after TT. However, there is currently no internationally accepted conclusion on this matter. The inconsistent results of previous studies may be attributable to data being obtained in different regions using different criteria, the use of various time points for determination of hypocalcemia, and the fact that some studies have included medullary thyroid carcinoma, follicular thyroid carcinoma, and toxic diffuse goiter. The rich blood supply in the thyroid gland means that patients with diffuse toxic goiter are at increased risk of intraoperative injury to the parathyroid gland, which increases the likelihood of postoperative hypocalcemia 20 . Furthermore, patients with medullary thyroid carcinoma are very likely to have hyperparathyroidism 21 , which may lead to removal of the parathyroid glands, again increasing the likelihood of postoperative hypocalcemia. Therefore, all patients included in our study were required to have PTC to avoid any bias resulting from the inclusion of different types of thyroid pathology. PTH is a peptide hormone synthesized and secreted by the parathyroid glands that mainly acts on target organs, including the bones and kidneys, and is involved in the regulation of Ca and P metabolism. Considering that PTH is the main biochemical indicator used to assess serum Ca levels, it has been widely studied as a risk factor for hypocalcemia after TT 22 – 24 . Researchers who have mainly focused on the ability of postoperative PTH levels and changes therein to predict hypocalcemia have measured PTH at times ranging from intraoperative skin closure up to 48 hours and even up to 4 days postoperatively 23 – 38 . These studies have suggested that both the percent change and absolute value of PTH levels at 10–20 minutes and less than 23 hours postoperatively can predict the development of hypocalcemia 26 – 32 , 36 , 38 . Additionally, the degree of decrease in PTH levels for a period of 4 hours postoperatively is a more accurate predictor of postoperative hypocalcemia following TT than PTH levels alone 30 , 38 , and at 4 versus 23 hours postoperatively or even at PODI, there may be no significant difference in PTH levels 29 , 36 . It has also been demonstrated that combining PODI PTH with PODI Ca levels may result in more reliable outcomes 39 , 40 . There are inherent difficulties in obtaining 100% long-term follow-up rates after patients are discharged from the hospital following surgery. As a result, data are largely incomplete, making the reported incidence of long-term complications unreliable. The UK Registry of Endocrine and Thyroid Surgeons have demonstrated a lack of data on complementary therapy at 6 months after thyroidectomy in nearly 22% of patients 41 . Therefore, a challenge remains in accurately identifying patients who develop permanent hypocalcemia, and it is important for surgeons to strengthen regular follow-up and detection of all patients after thyroid surgery. Similar to previous research, our univariate logistic regression analyses revealed that permanent hypocalcemia in patients after TT was significantly associated with PODI PTH, PODI Ca, PODI P, PTH change, Ca change and P change. However, we also found that follow-up data such as IM PTH, IM Ca, IM P, IIIM PTH, and IIIM Ca were associated with permanent hypocalcemia. Further multivariate logistic regression analyses also showed that IM PTH, IM Ca, and IM P may be independent risk factors for permanent hypocalcemia. Disturbances in serum magnesium metabolism may be closely related to postoperative hypocalcemia following TT 42 , 43 . If PODI Mg is less than 1.8 mg/dL, the patient requires Ca supplementation, suggesting that low Mg may indeed be associated with postoperative hypocalcemia 36 . It has been demonstrated that patients with hypomagnesaemia within 2 days of surgery have a significantly increased risk of developing postoperative temporary or even permanent hypocalcemia 44 . Furthermore, an Mg concentration below 0.78 mmol/L on postoperative day 2 (PODII) has been demonstrated to possess a certain predictive value for postoperative hypocalcemia, with a sensitivity and specificity of 71.2% and 77.5%, respectively 25 . In our study, we did not find a significant relationship between hypocalcemia and Mg levels for preoperative Mg, postoperative Mg and its change. This may be associated with the fact this was a single-center study with a smaller sample size 45 . A limited number of studies have investigated the potential of serum P levels as a predictor of postoperative hypocalcemia following TT. One study demonstrated that in comparison with the preoperative level, a 40% increase in blood P levels on PODII can predict the development of postoperative hypocalcemia on PODII, with an AUC of 0.80 and with sensitivity and specificity of 60.6% and 83.8%, respectively 46 . However, no significant difference between blood P levels and postoperative hypocalcemia was reported in other studies 25 , 28 . The results of our study indicated that P change and IM P may be associated with the risk of developing permanent hypocalcemia. In addition to the above indicators, vitamin D is involved in the regulation of serum Ca 47 . However, the effect of vitamin D on postoperative hypocalcemia is controversial 48 , 49 . Preoperative vitamin D deficiency may be associated with risk factors for postoperative hypocalcemia 50 , 51 . The lack of vitamin D monitoring in our study may affect the accuracy of the prediction models owing to financial constraints. Based on our research, the constructed prediction models took into account the influence of multiple factors on the occurrence of permanent hypocalcemia. Model 1 uses three metrics, IM PTH, IM Ca, and IM P. Model 2 additionally incorporates TNM staging to analyze the potential impact of pathological staging. According to the Delong test, there may not be a significant difference in the predictive value of these two models, and good predictive results can be achieved using model 1 alone, which is more conducive to clinical work. We further verified the consistency between the incidence of permanent postoperative hypocalcemia estimated using the predictive models and its actual incidence. Our study differs from previous studies that have only used independent indicators to predict the risk of developing permanent hypocalcemia. The constructed models can provide guidance and a reference for the follow-up period and key patient indicators that should be monitored after discharge. We also demonstrated the potential value of nomograms for assessing postoperative complications. The present study has some limitations. Because this was a single-center retrospective study, the sample size was limited by regional demographic characteristics. Also, the study population mainly included middle-aged and older patients, with few patients in the age group 18–30 years, which may have influenced our results. A lack of assessment of perioperative vitamin D and patients' nutritional status may have introduced bias into our findings. Furthermore, a lack of consensus on the definition of transient and permanent hypocalcemia may also affect the accuracy of the prediction models 8 , 34 , 52 , 53 . Despite the above limitations, we believe that our nomograms can be helpful for clinical work. A potential future avenue of research is the conduct of large-scale, multi-center cohort studies, which can enhance the model and facilitate the acquisition of greater external utility. Conclusions In this study, we established a nomogram to help clinicians predict the risk of permanent hypocalcemia in patients with PTC following TT. This tool could help clinicians quantify the potential incidence of permanent hypocalcemia. In the future, we will conduct additional multicenter prospective studies to improve the clinical application value of the nomogram. Declarations Competing interests The authors declare no competing interests. Funding None Author Contribution BHC and YY participated in writing the manuscript and analyzing the data. BHC and XCL participated in the design and conceptualization of the study. CGZ and MMJ approved the fnal version of the manuscript. All authors were involved in revising the manuscript and read and approved the submitted version. Acknowledgement We thank Analisa Avila, MPH, ELS, of Liwen Bianji (Edanz) (www.liwenbianji.cn) for editing the language of a draft of this manuscript. Data Availability The data used in this study can be made available upon reasonable request to the corresponding author. References Chen, D. W., Lang, B. H. H., McLeod, D. S. A., Newbold, K. & Haymart, M. R. Thyroid cancer. The Lancet 401, 1531–1544 (2023). Shoback, D. M. et al. 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Predictive value of postoperative day 1 parathyroid hormone levels for early and late hypocalcaemia after thyroidectomy. Langenbecks Arch Surg 407, 1653–1658 (2022). Carr, A. A. et al. A single parathyroid hormone level obtained 4 hours after total thyroidectomy predicts the need for postoperative calcium supplementation. J Am Coll Surg 219, 757–764 (2014). Lončar, I. et al. Postoperative parathyroid hormone levels as a predictor for persistent hypoparathyroidism. Eur J Endocrinol 183, 149–159 (2020). Suwannasarn, M., Jongjaroenprasert, W., Chayangsu, P., Suvikapakornkul, R. & Sriphrapradang, C. Single measurement of intact parathyroid hormone after thyroidectomy can predict transient and permanent hypoparathyroidism: a prospective study. Asian J Surg 40, 350–356 (2017). Nagel, K. et al. Definition and diagnosis of postsurgical hypoparathyroidism after thyroid surgery: meta-analysis. BJS Open 6, zrac102 (2022). Wang, X. et al. Postoperative hypoparathyroidism after thyroid operation and exploration of permanent hypoparathyroidism evaluation. Front Endocrinol (Lausanne) 14, 1182062 (2023). Chadwick, D. R. Hypocalcaemia and permanent hypoparathyroidism after total/bilateral thyroidectomy in the BAETS Registry. Gland Surg 6, S69–S74 (2017). Nellis, J. C., Tufano, R. P. & Gourin, C. G. Association between Magnesium Disorders and Hypocalcemia Following Thyroidectomy. Head and Neck Surgery . Liu, R. H. et al. Association of Hypocalcemia and Magnesium Disorders With Thyroidectomy in Commercially Insured Patients. JAMA Otolaryngol Head Neck Surg 146, 237–246 (2020). Brophy, C., Woods, R., Murphy, M. S. & Sheahan, P. Perioperative magnesium levels in total thyroidectomy and relationship to hypocalcemia. Head Neck 41, 1713–1718 (2019). Lorente-Poch, L., Sancho, J. J., Muñoz-Nova, J. L., Sánchez-Velázquez, P. & Sitges-Serra, A. Defining the syndromes of parathyroid failure after total thyroidectomy. Gland Surg 4, 82–90 (2015). Cho, J. N., Park, W. S. & Min, S. Y. Predictors and risk factors of hypoparathyroidism after total thyroidectomy. Int J Surg 34, 47–52 (2016). Mannstadt, M. et al. Hypoparathyroidism. Nat Rev Dis Primers 3, 17055 (2017). Unsal, I. O. et al. Preoperative Vitamin D Levels as a Predictor of Transient Hypocalcemia and Hypoparathyroidism After Parathyroidectomy. Sci Rep 10, 9895 (2020). Griffin, T. P., Murphy, M. S. & Sheahan, P. Vitamin D and risk of postoperative hypocalcemia after total thyroidectomy. JAMA Otolaryngol Head Neck Surg 140, 346–351 (2014). Erbil, Y. et al. The impact of age, vitamin D(3) level, and incidental parathyroidectomy on postoperative hypocalcemia after total or near total thyroidectomy. Am J Surg 197, 439–446 (2009). Bove, A. et al. Vitamin D Deficiency as a Predictive Factor of Transient Hypocalcemia after Total Thyroidectomy. Int J Endocrinol 2020, 8875257 (2020). Antakia, R., Edafe, O., Uttley, L. & Balasubramanian, S. P. Effectiveness of Preventative and Other Surgical Measures on Hypocalcemia Following Bilateral Thyroid Surgery: A Systematic Review and Meta-Analysis. Thyroid 25, 95–106 (2015). Harsløf, T., Rolighed, L. & Rejnmark, L. Huge variations in definition and reported incidence of postsurgical hypoparathyroidism: a systematic review. Endocrine 64, 176–183 (2019). Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 18 Mar, 2025 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 19 Dec, 2024 Reviews received at journal 16 Dec, 2024 Reviewers agreed at journal 13 Dec, 2024 Reviews received at journal 15 Nov, 2024 Reviewers agreed at journal 30 Oct, 2024 Reviewers agreed at journal 28 Sep, 2024 Reviewers invited by journal 13 Aug, 2024 Editor assigned by journal 13 Aug, 2024 Editor invited by journal 28 Jul, 2024 Submission checks completed at journal 25 Jul, 2024 First submitted to journal 20 Jul, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4774077","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":341642723,"identity":"d8ef5916-917f-4294-9f07-17ff06af1d7b","order_by":0,"name":"BoHan Cao","email":"","orcid":"","institution":"Benxi Central Hospital of China Medical University","correspondingAuthor":false,"prefix":"","firstName":"BoHan","middleName":"","lastName":"Cao","suffix":""},{"id":341642724,"identity":"c4471a4c-aab0-407f-8c58-d91c799cd53e","order_by":1,"name":"CanGang Zhang","email":"","orcid":"","institution":"Benxi Central Hospital of China Medical University","correspondingAuthor":false,"prefix":"","firstName":"CanGang","middleName":"","lastName":"Zhang","suffix":""},{"id":341642725,"identity":"e37bc19e-f576-44d3-9e67-319d8622b2d0","order_by":2,"name":"MingMing Jiang","email":"","orcid":"","institution":"Benxi Central Hospital of China Medical University","correspondingAuthor":false,"prefix":"","firstName":"MingMing","middleName":"","lastName":"Jiang","suffix":""},{"id":341642726,"identity":"6e443730-7864-4f14-9685-8ebaee2e2f69","order_by":3,"name":"Yi Yang","email":"","orcid":"","institution":"Shengjing Hospital of China Medical University","correspondingAuthor":false,"prefix":"","firstName":"Yi","middleName":"","lastName":"Yang","suffix":""},{"id":341642727,"identity":"6f6ea821-fb42-4f25-a236-2c9ef9e0c697","order_by":4,"name":"XiCai Liu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA1UlEQVRIiWNgGAWjYBACPmYgkcDAwGMA4iVUSMjJE9LChqrljIWxYQMhLTAGWAtjW0UiwwFCWth5zCQe1NjJmLP3HpN4OE8igbGB+eGjG3gdxmNskHAsmcey51yyQeI2iTx2BjZj4xz8WgwfJDYw8xjcyAEytkkUMzbwsEkT0GJwILGhnsfg/hsgY45EYsMBwlpAthwG2gJmEKWFrRjol+NAv+SAPCVhbNhMwC/8/Ie3Sf6oqbY3Zz9jBmTUycmzNz98jE8LFsBMmvJRMApGwSgYBVgAAOoGQDhN9Bp5AAAAAElFTkSuQmCC","orcid":"","institution":"Benxi Central Hospital of China Medical University","correspondingAuthor":true,"prefix":"","firstName":"XiCai","middleName":"","lastName":"Liu","suffix":""}],"badges":[],"createdAt":"2024-07-20 17:08:19","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4774077/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4774077/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-025-93867-9","type":"published","date":"2025-03-18T15:57:05+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":63370248,"identity":"4a3aea63-0911-4a18-9230-5951c9e7f3bb","added_by":"auto","created_at":"2024-08-27 11:49:28","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":58451,"visible":true,"origin":"","legend":"\u003cp\u003eModel 1 nomogram. PTH, parathyroid hormone; Ca, calcium; P, potassium; IM, postoperative month 1.\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-4774077/v1/0466d17a97074c0f0339e5df.jpeg"},{"id":63370240,"identity":"dde86acf-f898-4bcb-a050-89cd6a00f008","added_by":"auto","created_at":"2024-08-27 11:49:28","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":67118,"visible":true,"origin":"","legend":"\u003cp\u003eModel 2 nomogram. PTH, parathyroid hormone; Ca, calcium; P, potassium; IM, postoperative month 1; T, tumor; N, node.\u003c/p\u003e","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-4774077/v1/22cb342bd111e70b7238b7f7.jpeg"},{"id":63371616,"identity":"e01e783b-f84a-4057-a31c-476fc446e42e","added_by":"auto","created_at":"2024-08-27 11:57:28","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":146964,"visible":true,"origin":"","legend":"\u003cp\u003e(\u003cstrong\u003eA\u003c/strong\u003e) Model 1 nomogram receiver operating characteristic (ROC) curves generated from the training set; (\u003cstrong\u003eB\u003c/strong\u003e) model 1 nomogram ROC curves generated using the validation set. AUC, area under the ROC curve.\u003c/p\u003e","description":"","filename":"floatimage3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-4774077/v1/cfabb0ac21df768c89a135fe.jpeg"},{"id":63372176,"identity":"da2c5509-5c3d-4e4d-b3fb-1a1cf0530c70","added_by":"auto","created_at":"2024-08-27 12:05:28","extension":"jpeg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":156140,"visible":true,"origin":"","legend":"\u003cp\u003e(\u003cstrong\u003eA\u003c/strong\u003e) Model 2 nomogram receiver operating characteristic (ROC) curves generated from the training set; (\u003cstrong\u003eB\u003c/strong\u003e) model 2 nomogram ROC curves generated using the validation set. AUC, area under the ROC curve.\u003c/p\u003e","description":"","filename":"floatimage4.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-4774077/v1/5b250b8e9e42178767132518.jpeg"},{"id":63371614,"identity":"c36cf475-d6ee-433a-b07e-e4a30b1b36fc","added_by":"auto","created_at":"2024-08-27 11:57:28","extension":"jpeg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":175928,"visible":true,"origin":"","legend":"\u003cp\u003e(\u003cstrong\u003eA\u003c/strong\u003e) Calibration plot for the training set of Model 1; (\u003cstrong\u003eB\u003c/strong\u003e) calibration plot for the validation set of Model 1\u003c/p\u003e","description":"","filename":"floatimage5.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-4774077/v1/136eb186ea8c691e95d7a9f3.jpeg"},{"id":63370243,"identity":"8c6513de-6dc5-4cab-96b3-f7d6eb363267","added_by":"auto","created_at":"2024-08-27 11:49:28","extension":"jpeg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":183483,"visible":true,"origin":"","legend":"\u003cp\u003e(\u003cstrong\u003eA\u003c/strong\u003e) Calibration plot for the training set of Model 2; (\u003cstrong\u003eB\u003c/strong\u003e) calibration plot for the validation set of Model 2.\u003c/p\u003e","description":"","filename":"floatimage6.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-4774077/v1/cc19570d5010828b1809f03d.jpeg"},{"id":63371613,"identity":"f92c21bd-1e66-4c93-bc0a-5b7f0a802158","added_by":"auto","created_at":"2024-08-27 11:57:28","extension":"jpeg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":285180,"visible":true,"origin":"","legend":"\u003cp\u003e(\u003cstrong\u003eA\u003c/strong\u003e) Decision curve analysis (DCA) curves for the training set of Model 1 and 95% confidence interval (CI); (\u003cstrong\u003eB\u003c/strong\u003e) DCA curves for the validation set of Model 1 and 95% CI.\u003c/p\u003e","description":"","filename":"floatimage7.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-4774077/v1/545558b155d254234d2d3b0d.jpeg"},{"id":63370246,"identity":"27bd4f9f-c321-47cd-ab49-872d2e32ca7b","added_by":"auto","created_at":"2024-08-27 11:49:28","extension":"jpeg","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":245621,"visible":true,"origin":"","legend":"\u003cp\u003e(\u003cstrong\u003eA\u003c/strong\u003e) Decision curve analysis (DCA) curves for the training set of Model 1 and 95% confidence interval (CI); (\u003cstrong\u003eB\u003c/strong\u003e) DCA curves for the validation set of Model 1 and 95% CI.\u003c/p\u003e","description":"","filename":"floatimage8.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-4774077/v1/b8b42ed21047b66cc0c67cbe.jpeg"},{"id":79120402,"identity":"63c9b992-1d6e-4bdd-a42a-fc3fc55da115","added_by":"auto","created_at":"2025-03-24 16:06:44","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2460275,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4774077/v1/4153bc23-81a7-4faf-bc14-3ccacaf5721b.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Development and validation of risk prediction models for permanent hypocalcemia after total thyroidectomy in patients with papillary thyroid carcinoma","fulltext":[{"header":"Introduction","content":"\u003cp\u003eWith the increasing popularity of neck ultrasound, the detection and incidence of thyroid cancer is gradually increasing. According to the World Health Organization (WHO) International Agency for Research on Cancer, the incidence of thyroid cancer is the ninth highest in the world, and it is more common in women, at approximately 75%. The median age of patients with thyroid cancer is approximately 50 years, but thyroid cancer is also the most common malignant tumor in people aged 16 to 33 years. PTC accounts for approximately 80% of patients with thyroid malignancy. Surgery remains the primary treatment option for patients with a high suspicion and confirmed PTC by puncture cytology, and TT is the preferred treatment option for non-low risk patients\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. As with other surgical procedures, patients undergoing TT may experience postoperative complications. Of these, hypocalcemia has a considerable impact on patients' quality of life in the postoperative period. In 2016, the American Thyroid Association defined hypocalcemia following TT as transient if occurring within 6 months of surgery and permanent if lasting for 6 months or longer postoperatively. Hypoparathyroidism (HPT) is defined as hypocalcemia and inadequate PTH levels, and is diagnosed as a permanent HPT if it persists for 6 or more months after surgery\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. Most current research on permanent hypocalcemia and HPT recognizes 6 months postoperatively as the time point when the greatest changes occur\u003csup\u003e\u003cspan additionalcitationids=\"CR4 CR5 CR6 CR7\" citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e. Hypocalcemia can cause a number of adverse reactions in patients, including muscle twitching, cramps, tingling and numbness, as well as spasms of the facial muscles and wrists. In severe cases, hypocalcemia may induce epilepsy or even cardiac arrhythmias. It is also possible for patients with hypocalcemia to present with no obvious symptoms, although this may be associated with the development of adverse psychiatric symptoms\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e. The early detection of postoperative hypocalcemia and prediction of permanent hypocalcemia are of great importance to clinicians. Moreover, timely intervention will minimize the adverse effects on patients\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eNomograms are a common tool for assessing the prognosis of oncological diseases\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e,\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e. A nomogram is a pictorial representation of a complex mathematical formula. Nomograms use biological and clinical variables to graphically depict a statistical prognostic model that generates a probability of a clinical event for a given individual\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e,\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e. Also, nomograms can be used to predict the risk of postoperative complications\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e,\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e. In the context of hypocalcemia following TT, most previous studies have concentrated on the examination of risk factors and their predictive value. However, there have been few attempts to develop risk prediction models for the screening of patients at high risk of developing permanent hypocalcemia after TT. The aim of this study was to identify factors associated with permanent hypocalcemia and their follow-up after TT and incorporate these into a nomogram constructed based on a model for predicting permanent postoperative hypocalcemia.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy population\u003c/h2\u003e \u003cp\u003eIn total, 106 patients with malignant thyroid tumors who underwent TT between August 2021 and March 2023 were retrospectively identified in a search of the electronic medical records at Benxi Central Hospital of China Medical University. After 14 exclusions (one patient each with follicular thyroid carcinoma, medullary thyroid carcinoma, preoperative hypocalcemia, and combined hyperthyroidism; four patients with other diseases affecting postoperative Ca in the serum ; and six patients who were lost to follow-up after surgery), data for 92 patients with PTC were included in the statistical analysis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eEthics approval\u003c/h2\u003e \u003cp\u003e The study was approved by the Ethics Committee of Benxi Central Hospital of China Medical University. This study adhered to the tenets of the Declaration of Helsinki. This was a retrospective study, and the data used were obtained from the hospital's electronic medical records, with patient identifiers removed, so that patient privacy and related data would not be disclosed. The study was approved by the ethics committee of our institution, and informed consent was exempted.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eSurgical procedures\u003c/h2\u003e \u003cp\u003eAll patients underwent open surgery performed by the same surgical team using an anterior cervical approach with an ultrasonic scalpel. According to the Chinese and American guidelines for the diagnosis and treatment of differentiated thyroid cancer, TT includes central lymph node dissection (CLND) on at least one side of the thyroid gland with the upper border reaching the hyoid bone, the lower border flat on the plane of the innominate artery, the common carotid artery on the outer border, the more superficial deep cervical fascia on the anterior border, and the deeper deep cervical fascia on the posterior border. Lateral cervical lymph node dissection (LCLND) was performed if the lateral cervical region showed evidence of metastases. The lateral cervical region was routinely dissected in zones II to V, up to the digastric muscle, down to the superior margin of the clavicle, inward to the medial border of the carotid sheath, and outward to the anterior border of the trapezius muscle\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e,\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eEligibility criteria\u003c/h2\u003e \u003cp\u003eThe following inclusion criteria were applied: TT for the first time and postoperative pathological confirmation of PTC; pathology corresponding to the American Joint Committee on Cancer tumor, node, metastasis (TNM) stage; preoperative serum PTH, Ca, P, and magnesium (Mg) levels recorded and rechecked during follow-up at the same hospital; and complete medical and surgical records. Patients with any of the following were excluded: hepatic or renal insufficiency; hyperthyroidism and/or toxic diffuse goiter; preoperative parathyroid or other endocrine disorder; pregnancy and/or lactation; important follow-up data missing; or a disorder of Ca, P, or Mg metabolism before surgery or during follow-up.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eIndicators and definitions\u003c/h2\u003e \u003cp\u003eThe general data collected from patients included information on their age, sex, height, weight, and whether they had any underlying comorbidities associated with Hashimoto's thyroiditis (HT). Additionally, the surgical procedure and pathological staging were documented. Laboratory investigation data encompassed the patients' preoperative Ca, P, Mg, and PTH levels (formulated as the Pre\u0026thinsp;+\u0026thinsp;index), postoperative day 1 (PODI )(formulated as the PODI\u0026thinsp;+\u0026thinsp;index), and postoperative Ca, P, Mg, and PTH levels at 1, 3, and 6 months postoperatively. All blood samples were taken in the morning after an overnight fast.\u003c/p\u003e \u003cp\u003eDefining indicators were as follows: change in PTH=(Pre PTH\u0026thinsp;\u0026minus;\u0026thinsp;PODI PTH)/Pre PTH0; change in serum Ca=(Pre Ca\u0026thinsp;\u0026minus;\u0026thinsp;PODI Ca)/Ca0; change in serum Mg=(Pre Mg\u0026thinsp;\u0026minus;\u0026thinsp;PODI Mg)/Pre Mg0; change in serum P=(PODI P\u0026thinsp;\u0026minus;\u0026thinsp;Pre P)/Pre P0.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eStudy groups\u003c/h2\u003e \u003cp\u003eIncluded patients were divided into a permanent hypocalcemia group and a non-hypocalcemia group based on a serum Ca threshold of 2.10 mmol/L at 6 months postoperatively.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eMeasurements are expressed as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation. Counts are expressed as number (percentage). The data were randomly divided into a training set (n\u0026thinsp;=\u0026thinsp;65) and a validation set (n\u0026thinsp;=\u0026thinsp;27) according to a 7:3 ratio. Column line plots were used to illustrate the risk of permanent hypocalcemia in patients following TT. Multivariate logistic regression analyses were conducted to develop and validate the models. Initially, all data from the training and validation sets were analyzed to ascertain whether there was a statistically significant difference between the two sets. This was followed by an analysis of the training sets using univariate logistic regression to identify predictors of permanent hypocalcemia in patients following TT. Finally, the selected predictors were included in multifactorial logistic regression analyses and incorporated into nomograms. The predictive models were validated using discrimination, accuracy, and clinical validity. In this study, ROC curve analysis showed that AUC could be used to determine the discriminative power of the model.Calibration curves were used to determine the degree of agreement between predicted probabilities and observed outcomes. To assess the clinical validity of the model, decision curve analysis (DCA) was applied. The \u0026ldquo;pROC\u0026rdquo; package in R was used to plot ROC curves and the \u0026ldquo;RMDA\u0026rdquo; package was used to plot DCA curves. The \u0026ldquo;RMS\u0026rdquo; package was used to plot calibration curves and nomograms. All data analyses were conducted using R software version 4.3.0 (The R Project for Statistical Computing, Vienna, Austria), and P-values\u0026thinsp;\u0026lt;\u0026thinsp;0.05 were considered statistically significant.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eParticipant characteristics\u003c/h2\u003e \u003cp\u003eThe baseline characteristics of all patients as well as patients in the training and validation sets were assessed, and the training and validation sets were analyzed. No significant discrepancies were observed in the data between the training set and validation set (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eComparison between variables in the training and validation sets. \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 indicates statistical significance. BMI, body mass index; HT, Hashimoto's thyroiditis; PTH, parathyroid hormone; PODI, postoperative day 1; Ca, calcium; Mg, magnesium; P, potassium; CLND, central lymph node dissection; LCLND, lateral cervical lymph node dissection; T, tumor; N, node; M, metastasis; IM, postoperative month 1; IIIM, postoperative month 3; VIM, postoperative month 6; BCLND, bilateral cervical lymph node dissection.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTraining set\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eValidation set\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eP-\u003c/em\u003eValue\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003en\u0026thinsp;=\u0026thinsp;92\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003en\u0026thinsp;=\u0026thinsp;65\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003en\u0026thinsp;=\u0026thinsp;27\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePermanent Hypocalcemia(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e40(43.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e30(46.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10(37.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.567\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge(years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e49.80\u0026thinsp;\u0026plusmn;\u0026thinsp;9.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e48.80\u0026thinsp;\u0026plusmn;\u0026thinsp;9.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e52.15\u0026thinsp;\u0026plusmn;\u0026thinsp;9.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.132\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.447\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e72(78.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e49(75.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e23(85.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20(21.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16(24.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4(14.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHeight(cm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e163.89\u0026thinsp;\u0026plusmn;\u0026thinsp;7.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e164.18\u0026thinsp;\u0026plusmn;\u0026thinsp;8.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e163.19\u0026thinsp;\u0026plusmn;\u0026thinsp;6.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.688\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWeight(kg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e68.41\u0026thinsp;\u0026plusmn;\u0026thinsp;12.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e69.32\u0026thinsp;\u0026plusmn;\u0026thinsp;13.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e66.22\u0026thinsp;\u0026plusmn;\u0026thinsp;9.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.216\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI(kg/ m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e25.36\u0026thinsp;\u0026plusmn;\u0026thinsp;3.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25.56\u0026thinsp;\u0026plusmn;\u0026thinsp;3.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e24.88\u0026thinsp;\u0026plusmn;\u0026thinsp;3.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.391\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHT(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e29(33.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22(33.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7 (25.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.618\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePre PTH(pg/mL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e63.66\u0026thinsp;\u0026plusmn;\u0026thinsp;21.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e61.83\u0026thinsp;\u0026plusmn;\u0026thinsp;18.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e68.05\u0026thinsp;\u0026plusmn;\u0026thinsp;27.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.425\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePre Ca(mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.29\u0026thinsp;\u0026plusmn;\u0026thinsp;0.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.29\u0026thinsp;\u0026plusmn;\u0026thinsp;0.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.29\u0026thinsp;\u0026plusmn;\u0026thinsp;0.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.817\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePre Mg(mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.92\u0026thinsp;\u0026plusmn;\u0026thinsp;0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.91\u0026thinsp;\u0026plusmn;\u0026thinsp;0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.94\u0026thinsp;\u0026plusmn;\u0026thinsp;0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.091\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePre P(mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.17\u0026thinsp;\u0026plusmn;\u0026thinsp;0.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.19\u0026thinsp;\u0026plusmn;\u0026thinsp;0.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.13\u0026thinsp;\u0026plusmn;\u0026thinsp;0.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.315\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePODI PTH(pg/mL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e22.90\u0026thinsp;\u0026plusmn;\u0026thinsp;17.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e21.68\u0026thinsp;\u0026plusmn;\u0026thinsp;17.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e25.84\u0026thinsp;\u0026plusmn;\u0026thinsp;17.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.245\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePODI Ca(mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.05\u0026thinsp;\u0026plusmn;\u0026thinsp;0.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.05\u0026thinsp;\u0026plusmn;\u0026thinsp;0.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.06\u0026thinsp;\u0026plusmn;\u0026thinsp;0.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.955\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePODI Mg(mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.82\u0026thinsp;\u0026plusmn;\u0026thinsp;0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.81\u0026thinsp;\u0026plusmn;\u0026thinsp;0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.83\u0026thinsp;\u0026plusmn;\u0026thinsp;0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.251\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePODI P(mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.32\u0026thinsp;\u0026plusmn;\u0026thinsp;0.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.32\u0026thinsp;\u0026plusmn;\u0026thinsp;0.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.32\u0026thinsp;\u0026plusmn;\u0026thinsp;0.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.784\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePTH change\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.64\u0026thinsp;\u0026plusmn;\u0026thinsp;0.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.65\u0026thinsp;\u0026plusmn;\u0026thinsp;0.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.59\u0026thinsp;\u0026plusmn;\u0026thinsp;0.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.443\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCa change\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.10\u0026thinsp;\u0026plusmn;\u0026thinsp;0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.10\u0026thinsp;\u0026plusmn;\u0026thinsp;0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.1\u0026thinsp;\u0026plusmn;\u0026thinsp;0.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.859\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMg change\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.11\u0026thinsp;\u0026plusmn;\u0026thinsp;0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.11\u0026thinsp;\u0026plusmn;\u0026thinsp;0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.11\u0026thinsp;\u0026plusmn;\u0026thinsp;0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.849\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP change\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.04\u0026thinsp;\u0026plusmn;\u0026thinsp;0.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.03\u0026thinsp;\u0026plusmn;\u0026thinsp;0.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.05\u0026thinsp;\u0026plusmn;\u0026thinsp;0.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.786\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIM PTH(pg/mL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e51.69\u0026thinsp;\u0026plusmn;\u0026thinsp;20.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e49.27\u0026thinsp;\u0026plusmn;\u0026thinsp;19.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e57.51\u0026thinsp;\u0026plusmn;\u0026thinsp;22.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.110\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIM Ca(mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.18\u0026thinsp;\u0026plusmn;\u0026thinsp;0.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.18\u0026thinsp;\u0026plusmn;\u0026thinsp;0.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.21\u0026thinsp;\u0026plusmn;\u0026thinsp;0.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.461\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIM Mg(mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.90\u0026thinsp;\u0026plusmn;\u0026thinsp;0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.91\u0026thinsp;\u0026plusmn;\u0026thinsp;0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.9\u0026thinsp;\u0026plusmn;\u0026thinsp;0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.458\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIM P(mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.26\u0026thinsp;\u0026plusmn;\u0026thinsp;0.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.26\u0026thinsp;\u0026plusmn;\u0026thinsp;0.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.24\u0026thinsp;\u0026plusmn;\u0026thinsp;0.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.722\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIIIM PTH(pg/mL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e54.94\u0026thinsp;\u0026plusmn;\u0026thinsp;37.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e53.76\u0026thinsp;\u0026plusmn;\u0026thinsp;41.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e57.77\u0026thinsp;\u0026plusmn;\u0026thinsp;27.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.233\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIIIM Ca(mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.17\u0026thinsp;\u0026plusmn;\u0026thinsp;0.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.16\u0026thinsp;\u0026plusmn;\u0026thinsp;0.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.20\u0026thinsp;\u0026plusmn;\u0026thinsp;0.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.352\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIIIM Mg(mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.90\u0026thinsp;\u0026plusmn;\u0026thinsp;0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.89\u0026thinsp;\u0026plusmn;\u0026thinsp;0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.90\u0026thinsp;\u0026plusmn;\u0026thinsp;0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.478\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIIIM P(mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.23\u0026thinsp;\u0026plusmn;\u0026thinsp;0.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.23\u0026thinsp;\u0026plusmn;\u0026thinsp;0.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.22\u0026thinsp;\u0026plusmn;\u0026thinsp;0.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.767\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVIM PTH(pg/mL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e46.94\u0026thinsp;\u0026plusmn;\u0026thinsp;19.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e45.75\u0026thinsp;\u0026plusmn;\u0026thinsp;18.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e49.82\u0026thinsp;\u0026plusmn;\u0026thinsp;22.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.414\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVIM Ca(mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.13\u0026thinsp;\u0026plusmn;\u0026thinsp;0.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.12\u0026thinsp;\u0026plusmn;\u0026thinsp;0.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.14\u0026thinsp;\u0026plusmn;\u0026thinsp;0.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.352\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVIM Mg(mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.89\u0026thinsp;\u0026plusmn;\u0026thinsp;0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.89\u0026thinsp;\u0026plusmn;\u0026thinsp;0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.89\u0026thinsp;\u0026plusmn;\u0026thinsp;0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.867\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVIM P(mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.17\u0026thinsp;\u0026plusmn;\u0026thinsp;0.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.17\u0026thinsp;\u0026plusmn;\u0026thinsp;0.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.17\u0026thinsp;\u0026plusmn;\u0026thinsp;0.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.938\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSurgical procedure\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.307\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBCLND(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e72(78.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e53(81.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e19(70.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLeft CLND(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3(3.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3(4.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0(0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRight CLND(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5(5.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3(4.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2(7.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBCLND\u0026thinsp;+\u0026thinsp;Left LCLND(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7(7.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3(4.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4(14.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBCLND\u0026thinsp;+\u0026thinsp;Right LCLND(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5(5.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3(4.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2(7.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePathological staging\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.288\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTIa(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e58(63.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e41(63.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e17(63%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTIb (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e23(25.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18(27.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5(18.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTII (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4(4.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1(1.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3(11.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTIIIb (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6(6.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4(6.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2(7.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTIVa (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1(1.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1(1.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0(0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.412\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN0(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e65(70.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e45(69.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e20(74.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNIa (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e23(25.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18(27.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5(18.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNIb (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4(4.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2(3.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2(7.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eM0(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e92(100%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e65(100%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e27(100%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eResults of univariate and multivariate analyses\u003c/h2\u003e \u003cp\u003eUnivariate and multivariate logistic regression analyses were performed to identify risk factors for permanent hypocalcemia in 65 patients after TT. The parameters for which P\u0026thinsp;\u0026lt;\u0026thinsp;0.20 was calculated in univariate logistic regression analysis (PODI PTH, PODI Ca, PODI P, PTH change, Ca change, P change, postoperative month 1 [IM] PTH, IM Ca, IM Mg, IM P, postoperative month 3 [IIIM] PTH, IIIMCa and T stage) were included in multivariate logistic regression analysis. The results of this analysis identified IM PTH, IM Ca, and IM P (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05) as independent risk factors for permanent hypocalcemia in patients with PTC after TT (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eUnivariate and multivariate logistic regression analyses. BMI, body mass index; HT, Hashimoto's thyroiditis; PTH, parathyroid hormone; PODI, postoperative day 1; Ca, calcium; Mg, magnesium; P, potassium; CLND, central lymph node dissection; LCLND, lateral cervical lymph node dissection; T, tumor; N, node; M, metastasis; IM, postoperative month 1; IIIM, postoperative month 3; OR, odds ratio; CI, confidence interval; VIM, postoperative month 6; BCLND,bilateral cervical lymph node dissection.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003evariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eUnivariate analyse\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003eMultivariate analyse\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eP-Value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e Value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.953\u0026ndash;1.053\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.940\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.227\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.392\u0026ndash;3.857\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.723\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHeight(cm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.960\u0026ndash;1.088\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.503\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWeight(kg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.995\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.959\u0026ndash;1.031\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.778\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI(kg/ m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.936\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.814\u0026ndash;1.069\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.338\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.667\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.593\u0026ndash;4.778\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.333\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePre PTH(pg/mL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.978\u0026ndash;1.032\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.731\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePre Ca(mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.061\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u0026ndash;6.670\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.252\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePre Mg(mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.718\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.006-9339.537\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.597\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePre P(mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.124\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.158\u0026ndash;31.519\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.569\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePODI PTH(pg/mL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.965\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.931\u0026ndash;0.996\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.039\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.997\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.827\u0026ndash;1.211\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.969\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePODI Ca(mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.032\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.001\u0026ndash;0.877\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.050\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.345e\u0026thinsp;+\u0026thinsp;14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e8.874-1.643e\u0026thinsp;+\u0026thinsp;36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.089\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePODI Mg(mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.060\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.002-2529.993\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.838\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePODI P(mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.683\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.610-48.892\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.158\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.311\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001-2.296e\u0026thinsp;+\u0026thinsp;05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.737\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePTH change\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e27.342\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.988-347.751\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e43.196\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.002-1.90e\u0026thinsp;+\u0026thinsp;07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.490\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCa change\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e146.888\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.094-4.236e\u0026thinsp;+\u0026thinsp;05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.195\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.590e\u0026thinsp;+\u0026thinsp;27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3162.804-4.928e\u0026thinsp;+\u0026thinsp;67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.070\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMg change\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.779\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.004-936.644\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.855\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP change\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8.010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.098\u0026ndash;75.526\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.051\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e217.715\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.460-3.222e\u0026thinsp;+\u0026thinsp;06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.150\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIM PTH(pg/mL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.936\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.900-0.967\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.760\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.581\u0026ndash;0.895\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.008\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIM Ca(mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u0026ndash;0.012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.397e-08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e8.512e-17-0.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.019\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIM Mg(mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u0026ndash;3.890\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.130\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.126e\u0026thinsp;+\u0026thinsp;07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.139-3.027e\u0026thinsp;+\u0026thinsp;18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.123\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIM P(mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e88.497\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e26.853-69553.910\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.223e\u0026thinsp;+\u0026thinsp;07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e502.204-1.248e\u0026thinsp;+\u0026thinsp;15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.015\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIIIM PTH(pg/mL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.964\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.936\u0026ndash;0.988\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.894\u0026ndash;1.110\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.740\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIIIM Ca(mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.504e-05-0.069\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.356e-09-14.208\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.244\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIIIM Mg(mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.419\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001-990.781\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.825\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIIIM P(mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.970\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.501\u0026ndash;27.713\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.267\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSurgical procedure\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBCLND\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLeft CLND\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.604\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.027\u0026ndash;6.681\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.688\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRight CLND\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.604\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.027\u0026ndash;6.681\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.688\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBCLND\u0026thinsp;+\u0026thinsp;Left LCLND\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.417\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.219\u0026ndash;53.888\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.482\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBCLND\u0026thinsp;+\u0026thinsp;Right LCLND\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.417\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.219\u0026ndash;53.888\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.482\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePathological staging\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTIa\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTIb\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.455\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.801\u0026ndash;7.962\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.121\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.138\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.091-342.638\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.463\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTII\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.446e\u0026thinsp;+\u0026thinsp;07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.196e-205-NA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.994\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.730\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.134e-149-NA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.100\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTIIIb\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.563\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.173\u0026ndash;14.117\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.671\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.082\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u0026ndash;12.176\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.362\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTIVa\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNA-2.029e\u0026thinsp;+\u0026thinsp;205\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.854\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.081e-09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNA-1.403e\u0026thinsp;+\u0026thinsp;140\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.996\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNIa\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.563\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.521\u0026ndash;4.811\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.427\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNIb\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNA-2.94e\u0026thinsp;+\u0026thinsp;108\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.788\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003ePredictive model development\u003c/h2\u003e \u003cp\u003eThe prediction model comprised variables with P values\u0026thinsp;\u0026lt;\u0026thinsp;0.05 in multivariate logistic regression, which were IM PTH, IM Ca and IM P. Predictive model 1 was presented using a nomogram that can be used to quantitatively predict the risk of permanent hypocalcemia in patients after TT. Model 1 was as follows: Logit (p)\u0026thinsp;=\u0026thinsp;11.9478\u0026thinsp;\u0026minus;\u0026thinsp;0.0556\u0026times;IM PTH\u0026thinsp;\u0026minus;\u0026thinsp;8.3677\u0026times;IM Ca\u0026thinsp;+\u0026thinsp;7.3355\u0026times;IM P. Model 2 builds on this by incorporating pathological staging and investigating the effect of pathological staging on the predictive model. Model 2 was as follows: Logit (p)\u0026thinsp;=\u0026thinsp;9.0389\u0026thinsp;\u0026minus;\u0026thinsp;0.0556\u0026times;IM PTH\u0026thinsp;\u0026minus;\u0026thinsp;6.8710\u0026times;IM Ca\u0026thinsp;+\u0026thinsp;6.9819\u0026times;IM P\u0026thinsp;+\u0026thinsp;0.7505\u0026times;TIb\u0026thinsp;+\u0026thinsp;6.7132\u0026times;TII\u0026thinsp;+\u0026thinsp;0.1513\u0026times;TIIIb\u0026thinsp;\u0026minus;\u0026thinsp;7.1288\u0026times;TIVa\u0026thinsp;\u0026minus;\u0026thinsp;0.5418\u0026times;NIa\u0026thinsp;\u0026minus;\u0026thinsp;7.3310\u0026times;NIb. To increase the usefulness of these models, we generated nomograms showing scores that correspond to each risk factor and a total of all risk factors, corresponding to the risk of permanent hypocalcemia (Figs.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e)\u003c/p\u003e \u003cp\u003eThe application of the nomograms was as follows. According to the figures, the score value corresponding to each prediction index was found and recorded as the total score. The prediction probability corresponding to the total score in the last row of the figure is the risk of permanent hypocalcemia (range 0\u0026ndash;1).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003ePredictive model validation\u003c/h2\u003e \u003cdiv id=\"Sec15\" class=\"Section3\"\u003e \u003ch2\u003eDiscrimination\u003c/h2\u003e \u003cp\u003eAUC values of the ROC curves were calculated to assess the discrimination of the predictive model by examining the occurrence of permanent hypocalcemia in patients following TT in the training and validation sets of model 1 and 2. As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA and \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB, the predictive model 1 yielded an AUC value of 0.905 (95% CI\u0026thinsp;=\u0026thinsp;0.828\u0026ndash;0.982), with a specificity of 0.914, and sensitivity of 0.767 in the training set, and an AUC\u0026thinsp;=\u0026thinsp;0.894 (95% CI\u0026thinsp;=\u0026thinsp;0.777\u0026ndash;1.000), with a specificity of 0.706 and sensitivity of 1.00 in the validation set. Predictive model 2 had an AUC value of 0.913 (95% CI\u0026thinsp;=\u0026thinsp;0.844\u0026ndash;0.982), with a specificity of 0.886 and sensitivity of 0.800 in the training set, and AUC\u0026thinsp;=\u0026thinsp;0.800 (95% CI\u0026thinsp;=\u0026thinsp;0.630\u0026ndash;0.970), with a specificity of 0.706 and sensitivity of 0.900 in the validation set (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA and \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB). These data indicated that the two nomograms have good discriminatory ability and predictive value and can correctly identify patients at risk of permanent hypocalcemia.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eDelong test results\u003c/h2\u003e \u003cp\u003eThe Delong test was used to determine statistically significant differences in the ROC curves. The Delong test was used for the validation set of models 1 and 2 (Z\u0026thinsp;=\u0026thinsp;1.199, 95% confidence interval [CI]\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.060\u0026ndash;0.248, P\u0026thinsp;=\u0026thinsp;0.231). The result showed no statistically significant differences between the two ROC curves and the AUC. Model 2 included TNM staging, indicating that whether the model includes TNM staging makes little difference to the predictive value. Thus, model 1 was found to be more concise than model 2, which is more conducive to clinical application.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eCalibration of the predictive models\u003c/h2\u003e \u003cp\u003eWe constructed calibration curves for the models. The curves for models 1 and 2 in the training set showed that the curve performed well with an additional 1000 bootstraps but performed slightly worse in the validation set (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA and \u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB, \u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA and \u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eB)\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eEvaluation of clinical validity\u003c/h2\u003e \u003cp\u003eThe clinical validity of the model was evaluated using DCA; the results are shown in Figs.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eA, \u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eB, \u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eA, and \u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eB. According to DCA, the net benefits of the predictive models were significantly higher than those of the two extremes. Thus, it could be concluded that the prediction models were clinically effective.\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eHPT is the most common complication following TT. Biochemical HPT is defined as a low intact PTH level, below the lower limit of the laboratory standard, accompanied by hypocalcemia. Hypocalcemia is a total serum Ca level less than the lower limit of the center-specific reference range\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e,\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e,\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e. Hypocalcemia has the potential to cause adverse effects on both the physiological and psychological well-being of patients. Therefore, it is crucial to identify the risk of developing permanent hypocalcemia in the early postoperative period and during follow-up to implement timely interventions and appropriate psychological treatment and improve the patient's quality of life. A number of studies have been conducted by researchers in various countries on the risk factors of postoperative hypocalcemia following TT. The results of these studies indicate that age, sex, serum Mg, vitamin D, HT, and a low postoperative PTH level may be risk factors for postoperative hypocalcemia after TT. However, there is currently no internationally accepted conclusion on this matter. The inconsistent results of previous studies may be attributable to data being obtained in different regions using different criteria, the use of various time points for determination of hypocalcemia, and the fact that some studies have included medullary thyroid carcinoma, follicular thyroid carcinoma, and toxic diffuse goiter. The rich blood supply in the thyroid gland means that patients with diffuse toxic goiter are at increased risk of intraoperative injury to the parathyroid gland, which increases the likelihood of postoperative hypocalcemia\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e. Furthermore, patients with medullary thyroid carcinoma are very likely to have hyperparathyroidism\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e, which may lead to removal of the parathyroid glands, again increasing the likelihood of postoperative hypocalcemia. Therefore, all patients included in our study were required to have PTC to avoid any bias resulting from the inclusion of different types of thyroid pathology.\u003c/p\u003e \u003cp\u003ePTH is a peptide hormone synthesized and secreted by the parathyroid glands that mainly acts on target organs, including the bones and kidneys, and is involved in the regulation of Ca and P metabolism. Considering that PTH is the main biochemical indicator used to assess serum Ca levels, it has been widely studied as a risk factor for hypocalcemia after TT\u003csup\u003e\u003cspan additionalcitationids=\"CR23\" citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e. Researchers who have mainly focused on the ability of postoperative PTH levels and changes therein to predict hypocalcemia have measured PTH at times ranging from intraoperative skin closure up to 48 hours and even up to 4 days postoperatively\u003csup\u003e\u003cspan additionalcitationids=\"CR24 CR25 CR26 CR27 CR28 CR29 CR30 CR31 CR32 CR33 CR34 CR35 CR36 CR37\" citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e. These studies have suggested that both the percent change and absolute value of PTH levels at 10\u0026ndash;20 minutes and less than 23 hours postoperatively can predict the development of hypocalcemia\u003csup\u003e\u003cspan additionalcitationids=\"CR27 CR28 CR29 CR30 CR31\" citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e,\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e,\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e. Additionally, the degree of decrease in PTH levels for a period of 4 hours postoperatively is a more accurate predictor of postoperative hypocalcemia following TT than PTH levels alone\u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e,\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e, and at 4 versus 23 hours postoperatively or even at PODI, there may be no significant difference in PTH levels\u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e,\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e. It has also been demonstrated that combining PODI PTH with PODI Ca levels may result in more reliable outcomes\u003csup\u003e\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e,\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThere are inherent difficulties in obtaining 100% long-term follow-up rates after patients are discharged from the hospital following surgery. As a result, data are largely incomplete, making the reported incidence of long-term complications unreliable. The UK Registry of Endocrine and Thyroid Surgeons have demonstrated a lack of data on complementary therapy at 6 months after thyroidectomy in nearly 22% of patients\u003csup\u003e\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u003c/sup\u003e. Therefore, a challenge remains in accurately identifying patients who develop permanent hypocalcemia, and it is important for surgeons to strengthen regular follow-up and detection of all patients after thyroid surgery. Similar to previous research, our univariate logistic regression analyses revealed that permanent hypocalcemia in patients after TT was significantly associated with PODI PTH, PODI Ca, PODI P, PTH change, Ca change and P change. However, we also found that follow-up data such as IM PTH, IM Ca, IM P, IIIM PTH, and IIIM Ca were associated with permanent hypocalcemia. Further multivariate logistic regression analyses also showed that IM PTH, IM Ca, and IM P may be independent risk factors for permanent hypocalcemia.\u003c/p\u003e \u003cp\u003eDisturbances in serum magnesium metabolism may be closely related to postoperative hypocalcemia following TT\u003csup\u003e\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e,\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u003c/sup\u003e. If PODI Mg is less than 1.8 mg/dL, the patient requires Ca supplementation, suggesting that low Mg may indeed be associated with postoperative hypocalcemia\u003csup\u003e\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e. It has been demonstrated that patients with hypomagnesaemia within 2 days of surgery have a significantly increased risk of developing postoperative temporary or even permanent hypocalcemia\u003csup\u003e\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u003c/sup\u003e. Furthermore, an Mg concentration below 0.78 mmol/L on postoperative day 2 (PODII) has been demonstrated to possess a certain predictive value for postoperative hypocalcemia, with a sensitivity and specificity of 71.2% and 77.5%, respectively\u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e. In our study, we did not find a significant relationship between hypocalcemia and Mg levels for preoperative Mg, postoperative Mg and its change. This may be associated with the fact this was a single-center study with a smaller sample size\u003csup\u003e\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u003c/sup\u003e. A limited number of studies have investigated the potential of serum P levels as a predictor of postoperative hypocalcemia following TT. One study demonstrated that in comparison with the preoperative level, a 40% increase in blood P levels on PODII can predict the development of postoperative hypocalcemia on PODII, with an AUC of 0.80 and with sensitivity and specificity of 60.6% and 83.8%, respectively\u003csup\u003e\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e\u003c/sup\u003e. However, no significant difference between blood P levels and postoperative hypocalcemia was reported in other studies\u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e,\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e. The results of our study indicated that P change and IM P may be associated with the risk of developing permanent hypocalcemia.\u003c/p\u003e \u003cp\u003eIn addition to the above indicators, vitamin D is involved in the regulation of serum Ca\u003csup\u003e\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e\u003c/sup\u003e. However, the effect of vitamin D on postoperative hypocalcemia is controversial\u003csup\u003e\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e,\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e\u003c/sup\u003e. Preoperative vitamin D deficiency may be associated with risk factors for postoperative hypocalcemia\u003csup\u003e\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e,\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e\u003c/sup\u003e. The lack of vitamin D monitoring in our study may affect the accuracy of the prediction models owing to financial constraints.\u003c/p\u003e \u003cp\u003eBased on our research, the constructed prediction models took into account the influence of multiple factors on the occurrence of permanent hypocalcemia. Model 1 uses three metrics, IM PTH, IM Ca, and IM P. Model 2 additionally incorporates TNM staging to analyze the potential impact of pathological staging. According to the Delong test, there may not be a significant difference in the predictive value of these two models, and good predictive results can be achieved using model 1 alone, which is more conducive to clinical work. We further verified the consistency between the incidence of permanent postoperative hypocalcemia estimated using the predictive models and its actual incidence. Our study differs from previous studies that have only used independent indicators to predict the risk of developing permanent hypocalcemia. The constructed models can provide guidance and a reference for the follow-up period and key patient indicators that should be monitored after discharge. We also demonstrated the potential value of nomograms for assessing postoperative complications.\u003c/p\u003e \u003cp\u003eThe present study has some limitations. Because this was a single-center retrospective study, the sample size was limited by regional demographic characteristics. Also, the study population mainly included middle-aged and older patients, with few patients in the age group 18\u0026ndash;30 years, which may have influenced our results. A lack of assessment of perioperative vitamin D and patients' nutritional status may have introduced bias into our findings. Furthermore, a lack of consensus on the definition of transient and permanent hypocalcemia may also affect the accuracy of the prediction models\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e,\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e,\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e,\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e\u003c/sup\u003e. Despite the above limitations, we believe that our nomograms can be helpful for clinical work. A potential future avenue of research is the conduct of large-scale, multi-center cohort studies, which can enhance the model and facilitate the acquisition of greater external utility.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eIn this study, we established a nomogram to help clinicians predict the risk of permanent hypocalcemia in patients with PTC following TT. This tool could help clinicians quantify the potential incidence of permanent hypocalcemia. In the future, we will conduct additional multicenter prospective studies to improve the clinical application value of the nomogram.\u003c/p\u003e "},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eCompeting interests\u003c/h2\u003e \u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eNone\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eBHC and YY participated in writing the manuscript and analyzing the data. BHC and XCL participated in the design and conceptualization of the study. CGZ and MMJ approved the fnal version of the manuscript. All authors were involved in revising the manuscript and read and approved the submitted version.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eWe thank Analisa Avila, MPH, ELS, of Liwen Bianji (Edanz) (www.liwenbianji.cn) for editing the language of a draft of this manuscript.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe data used in this study can be made available upon reasonable request to the corresponding author.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eChen, D. W., Lang, B. H. H., McLeod, D. S. A., Newbold, K. \u0026amp; Haymart, M. R. Thyroid cancer. The Lancet 401, 1531\u0026ndash;1544 (2023).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShoback, D. M. \u003cem\u003eet al.\u003c/em\u003e Presentation of Hypoparathyroidism: Etiologies and Clinical Features. 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Endocrine 64, 176\u0026ndash;183 (2019).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Predictive model, Nomogram, Papillary thyroid carcinoma, Total thyroidectomy, Hypocalcemia","lastPublishedDoi":"10.21203/rs.3.rs-4774077/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4774077/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eHypocalcemia is a common complication and can be permanent in patients following total thyroidectomy (TT). The aim of this study was to identify factors associated with permanent hypocalcemia and to develop a validated risk prediction model for permanent hypocalcemia to assist surgeons in the appropriate follow-up of high-risk patients regarding supplemental therapy. We included data of 92 patients with papillary thyroid carcinoma (PTC) undergoing TT who were randomly allocated in a 7:3 ratio to a training set (n = 65) and validation set (n = 27). Univariate and multivariate logistic regression analyses revealed significant correlations of permanent hypocalcemia with parathyroid hormone (PTH) at postoperative month 1 (IM PTH), IM calcium (Ca), and IM phosphorus (P). These variables were constructed two models. Model 1 used the three indicators listed above; model 2 also included tumor, node, metastasis staging. The receiver operating characteristic (ROC) curve analysis showed that the areas under the curve (AUC) for models 1 and 2 were high for both the training set (0.905/0.913) and the validation set (0.894/0.800). Calibration curves showed good agreement between the incidence of permanent hypocalcemia estimated using the predictive models and the actual incidence. Model 1 may be more concise and convenient for clinical use.\u003c/p\u003e","manuscriptTitle":"Development and validation of risk prediction models for permanent hypocalcemia after total thyroidectomy in patients with papillary thyroid carcinoma","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-08-27 11:49:23","doi":"10.21203/rs.3.rs-4774077/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-12-19T11:05:12+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-12-16T08:38:02+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"140303219377226794692720396683670079675","date":"2024-12-13T10:21:11+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-11-15T22:27:55+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"18372494340431777743728338288732545490","date":"2024-10-30T18:05:12+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"196947332653678786805243732881243445946","date":"2024-09-28T08:09:51+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-08-13T07:48:14+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-08-13T07:45:54+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2024-07-28T18:43:11+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-07-25T14:13:11+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2024-07-20T16:58:54+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"3ace1398-b4fc-4dfb-b332-9847d876a79a","owner":[],"postedDate":"August 27th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":36198136,"name":"Health sciences/Endocrinology"},{"id":36198137,"name":"Health sciences/Risk factors"}],"tags":[],"updatedAt":"2025-03-24T15:59:44+00:00","versionOfRecord":{"articleIdentity":"rs-4774077","link":"https://doi.org/10.1038/s41598-025-93867-9","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2025-03-18 15:57:05","publishedOnDateReadable":"March 18th, 2025"},"versionCreatedAt":"2024-08-27 11:49:23","video":"","vorDoi":"10.1038/s41598-025-93867-9","vorDoiUrl":"https://doi.org/10.1038/s41598-025-93867-9","workflowStages":[]},"version":"v1","identity":"rs-4774077","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4774077","identity":"rs-4774077","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}