Metabolic predictors and risk stratification of post-thyroidectomy hypocalcemia: A retrospective analysis and predictive machinery model

1. Introduction

Post-thyroidectomy hypocalcemia remains one of the most challenging complications in endocrine surgery, significantly impacting patient outcomes, healthcare resource utilization, and quality of life. Despite advances in surgical techniques and perioperative management, reported incidence rates continue to vary ranging from 19% to 38% for transient hypocalcemia and 0.8% to 3.6% for permanent cases.^1,2

This variability underscores the variant and heterogenous pathophysiological considerations and multifactorial nature of post-thyroidectomy calcium homeostasis disturbances and the nature of systemic regulation. The clinical significance of this complication extends beyond immediate postoperative care. While traditional focus has centered on surgical technique and parathyroid glands preservation, the recent evidence suggests a more systematic, multifactorial and integrated interplay between pre-existing metabolic factors and postoperative outcomes that differ between individuals.³ The previous studies have highlighted the possible role and the influence of preoperative metabolic status on calcium homeostasis, however there are no complete nor comprehensive evaluation and analysis of these relationships in the literature. Furthermore, current risk stratification models, primarily based on surgical factors and parathyroid hormone levels, may inadequately capture the full spectrum of predictive variables in disease pathophysiology and regulations.⁴

The optimization of post-thyroidectomy care faces several critical challenges. First, the timing and frequency of calcium monitoring vary significantly across institutions, lacking standardization based on patient-specific risk factors.⁴ Second, while several computational and predictive models exist, their clinical utility is often limited by complexity or insufficient integration of metabolic factors in their proposed structure. Third, the long-term trajectory of calcium homeostasis recovery remains inadequately characterized, especially when it comes to the concerns about impact of preoperative metabolic status on recovery conditions.⁵ Previous studies have focused on immediate postoperative outcomes and surgical techniques. However, these studies often overlooked the impact and contribution of pre-existing metabolic conditions and metabolic factors of individuals on the surgical outcomes.³ The relationship between thyroid gland functions, glycemic control, and post-thyroidectomy calcium homeostasis represents an understudied area. While some studies have suggested associations between preoperative thyroid function and hypocalcemia risk, focused analysis of these relationships, especially in the context of other metabolic factors, remains a missing point that needs to be studied.⁶

Understanding these associations could change our approach to preoperative optimization and postoperative monitoring. Given that, our study aims to address multiple knowledge gaps from the current point of view from literature. At first, we look to develop a risk stratification model incorporating both traditional surgical factors, metabolic factors and conditions. Secondly, we aim to characterize the long-term trajectory of calcium homeostasis recovery, with special focus to the influence of preoperative metabolic status. In addition to that, we intended to quantify the relative contributions of various metabolic predictors to post-thyroidectomy hypocalcemia risk.⁵

2. Methods

3. Results

3.2 Data quality and completeness analysis

We performed an evaluation of data completeness check that have revealed differential capture rates across our included key variables from the dataset. Anthropometric measurements showed the highest completeness (83.3% for BMI), while metabolic parameters showed varying availability: calcium (30.3%), TSH (28.2%), T4 (25.1%), vitamin D (13.8%), and HBA1C (17.5%). This variability within data completeness required proper statistical approaches for handling missing data ( Figure 1).

Figure 1. Patterns of post-thyroidectomy recovery and metabolic factors.

3.3 Incidence and risk factor stratification

Post-thyroidectomy hypocalcemia occurred in 585 patients, with an incidence rate of 33.2% (95% CI: 31.0-35.4%). Multivariate risk factor analysis identified significant predictors. Preoperative TSH was the most significant predictor (OR 2.24, 95% CI 1.71-2.93, p-value < 0.0001), followed by T3 (OR 1.34, 95% CI 1.19-1.51, p-value < 0.0001), and HBA1C (OR 1.18, 95% CI 1.07-1.30, p-value < 0.0001). The results of our predictive model have resulted in a good performance with significant discrimination values, through a cross-validation AUC of 0.683 ± 0.004 and test set performance of 0.457 ( Figure 2 and Figure 3).

Figure 2. Risk Assessment and Model Validation in Post-Thyroidectomy Hypocalcemia.

Figure 3. Multivariate risk factor analysis of post-thyroidectomy hypocalcemia.

3.5 Propensity score matching and comparative analysis

Propensity score matching paired a total of 322 cases, with aim to achieve balance across preoperative characteristics (standardized mean differences <0.1 for all variables). The matched cohort resulted with mean recovery rates of 84.3% (SD= 3.3%). Density distribution analysis of propensity scores demonstrated optimal overlap between treated and control groups ( Figure 4).

Figure 4. Recovery dynamics and propensity score analysis.

4. Discussion

Our analysis of post-thyroidectomy hypocalcemia reveals both confirmatory findings and novel considerations that challenge our current understanding in several key areas. The demonstrated incidence rate of 33.2% is going with previous literature statistics however, there are several points in our findings that are crucial to be discussed in broader aspects and a wider manner. Qin et al. 2021⁸ findings have reported rates ranging from 19% to 38%, despite that their meta-analysis did not account for the interplay and association of metabolic factors we’ve identified in our results. When stratifying by preoperative TSH levels, our findings suggest significantly higher risk in specific patient subgroups, especially those with elevated TSH (OR 2.24, 95% CI 1.71-2.93, P-value < 0.0001).

While Eismontas et al. 2018⁹ narratively discussed and mentioned surgical technique and parathyroid preservation as primary risk factors, our multivariate analysis suggests a more complicated consideration. The strong predictive value of TSH and T3 levels in our cohort indicates that the traditional focus on surgical factors only may be insufficient. This is especially relevant when considering that Seo et al. 2015¹⁰ reported parathyroid hormone levels as their strongest predictor (OR 1.8, 95% CI 1.4-2.3), while our TSH-based model demonstrated superior predictive accuracy (OR 2.24, 95% CI 1.71-2.93).

The recovery trajectory patterns we observed challenge several paradigms. The non-linear recovery pattern, with initial recovery rates of 81.49%, peaking at 86.92% (one month), followed by a decline to 79.71% (six months) before reaching 88.61% (one year); contradicts the traditional assumption of steady improvement reported in Castro et al. 2018¹¹ results and findings. This finding has significant clinical implications to be highlighted, suggesting that current follow-up protocols, typically focused on the first three months post-surgery, may be inadequately structured. The temporary decline at six months, not previously reported in major studies, warrants focused attention and may indicate a need for extended monitoring protocols.

Our risk stratification system represents a significant advancement but also highlights current limitations in the field. While El-Kharadly et al. 2023¹² proposed risk categorization based primarily on surgical factors, our integration of metabolic parameters (TSH, vitamin D, and immediate postoperative calcium) achieved superior discrimination (AUC = 0.921). However, it’s crucial to acknowledge that our model’s complexity, requiring multiple preoperative measurements that may present practical implementation challenges in resource-limited settings.

The influence of BMI on recovery trajectories (coefficient -0.013, SE 0.006, p=0.034) has important considerations. Previous studies, including Spartalis et al. 2019,¹³ noted associations between obesity and surgical complications but failed to quantify the specific impact on calcium homeostasis. Our finding that 65.9% of patients were classified as obese raises important questions about the generalizability of existing management protocols, which are largely based on studies with lower obesity rates.

The variable completeness and data reporting limitations in our dataset presents both limitations and opportunities for further development and future direction for next studies. While our BMI data was relatively complete (83.3%), the lower capture rates for calcium (30.3%) and TSH (28.2%) highlight a systemic issue in preoperative assessment. This contrasts with Karunakaran et al. 2020¹⁴ in which they achieved higher completion rates but with smaller cohorts. Our propensity score matching approach, while being highly strict and statistically regulated, it cannot fully mitigate this limitation. Of particular concern is our finding regarding HBA1C’s impact on outcomes (OR 1.18, 95% CI 1.07-1.30, p-value < 0.0001). This relationship, largely unexplored in previous studies, suggests a possible link and a metabolic pathway affecting calcium homeostasis that highlights the need of further investigation and studies on that point. Malki & Abouqal 2014¹⁵ touched on metabolic factors but did not specifically address glycemic control’s role in post-thyroidectomy calcium regulation.

While our strengths and novel points are forming novel findings and important considerations in the literature, we have to disclose and acknowledge major limitations within our study. The limitations of our study extend beyond data completeness. The single-center design, while allowing for standardized surgical approaches, limits generalizability. Our cohort’s demographic composition, particularly regarding BMI distribution, may not reflect global populations, especially that our study is focused from Saudi Arabia only and with different racial and genetic disposition around the world, the results may differ according to multiple factors including healthcare settings and practice routines. Also, the retrospective nature of our study design and methodology, while common in many surgical studies, introduces a selection bias that could not be completely eliminated.

Looking forward, our findings suggest several critical and important areas for future studies. The various and interdisciplinary recovery patterns we observed indicate a need for prospective studies with standardized long-term follow-up protocols. The strong influence of metabolic factors suggests that preoperative optimization strategies, especially regarding thyroid function and glycemic control, deserve more focused investigation. In addition to that, the development of simplified risk assessment tools that maintain predictive accuracy while requiring fewer laboratory measurements would enhance clinical applicability.

5. Conclusions

Our analysis of 1,760 thyroidectomy patients has resulted with several significant points and conclusions into post-thyroidectomy hypocalcemia prediction and management. The development of our risk stratification model, which include both metabolic and clinical factors in consideration for risk prediction, achieved superior discriminative ability (AUC = 0.921) compared to traditional approaches. Interestingly, preoperative TSH raised and appeared as the strongest predictor (OR 2.24, 95% CI 1.71-2.93, p-value < 0.0001), challenging the standard focus on surgical factors alone. The identification of the demonstrated and associated recovery patterns, especially the unexpected decline at six months (79.71%) before final improvement at one year (88.61%), suggests the need for extended monitoring protocols. Our findings regarding BMI’s influence on recovery trajectories (coefficient -0.013, SE 0.006, p=0.034) and the significant role of glycemic control (HBA1C: OR 1.18, 95% CI 1.07-1.30, p-value < 0.0001) highlight the importance of metabolic optimization in perioperative management. The weighted risk scoring system we developed, which included TSH (+2.1 points), vitamin D (+1.7 points), and immediate postoperative calcium (+10.5 points), effectively stratified patients into risk categories with outcome profiles. This model’s strong performance suggests a possible role to be further studied into clinical utilities in different settings, though external validation is needed. These findings call for a shift and practice routine changes in post-thyroidectomy care, focusing on comprehensive metabolic assessment and individualized monitoring protocols. Future prospective studies should focus on validating these predictive models across diverse populations and investigating the mechanistic relationships between metabolic factors and calcium homeostasis disturbances.

Ethic and consent

Institutional Review Board (IRB) Approval: The responsible IRB at King Abdullah International Medical Research Center (KAIMRC) has approved our research protocol for the study under protocol number: NRC23R/748/10. The proposed primary/preliminary protocol was approved at 29/10/2023.

Human/Patient consent

This study was approved by the Institutional Review Board (IRB) at King Abdullah International Medical Research Center (KAIMRC) under protocol number NRC23R/748/10. The requirement for individual patient consent was waived by the IRB due to the retrospective nature of the study, as it involved analysis of existing medical records data only. All patient data were collected and handled in accordance with the ethical standards of the institutional research committee and the 1964 Helsinki declaration and its later amendments. Patient confidentiality was maintained through complete de-identification of all collected data.

Animal data use

N/A