The predictive value of routine preoperative laboratory parameters in patients with sporadic and solitary parathyroid adenoma

OBJECTIVE

Primary hyperparathyroidism (pHPT) is a disease in which the diagnosis depends increasingly on laboratory tests since the majority of patients are elderly people without typical symptoms. The aim of this study was to calculate the specificity and sensitivity rates, and positive predictive value of blood Ca, P, ALP, and iPTH levels in patients with PA.

MATERIALS AND METHODS

We calculated the specificity and sensitivity rates, and positive predictive value of blood calcium (Ca), phosphorous (P), alkaline phosphatase (ALP), and intact parathormone (iPTH) levels in patients with sporadic and solitary parathyroid adenoma (PA).

RESULTS

Twenty six patients with PA were included in the study. The mean age was 44.5±14.9 (18-70) years. Blood levels of Ca, P, ALP, and iPTH were 2.5±0.3 mmol/L, 0.8±0.3 mmol/L, 21.4±31.5 μkat/L, and 518.0±633.4 ng/L, respectively. With respect to the diagnosis of pHPT, the sensitivity of blood levels of iPTH, P, Ca, and ALP were 96%, 94%, 81%, and 73%, respectively. The specificity and positive predictive value of all parameters were 100% for the diagnosis of PA.

CONCLUSION

Routine laboratory parameters are specific and predictive for pHPT. However, the sensitivities of P and iPTH are higher than those other parameters.

Novel approach exploring the correlation between presepsin and routine laboratory parameters using explainable artificial intelligence

Although presepsin, a crucial biomarker for the diagnosis and management of sepsis, has gained prominence in contemporary medical research, its relationship with routine laboratory parameters, including demographic data and hospital blood test data, remains underexplored. This study integrates machine learning with explainable artificial intelligence (XAI) to provide insights into the relationship between presepsin and these parameters. Advanced machine learning classifiers provide a multilateral view of data and play an important role in highlighting the interrelationships between presepsin and other parameters. XAI enhances analysis by ensuring transparency in the model's decisions, especially in selecting key parameters that significantly enhance classification accuracy. Utilizing XAI, this study successfully identified critical parameters that increased the predictive accuracy for sepsis patients, achieving a remarkable ROC AUC of 0.97 and an accuracy of 0.94. This breakthrough is possibly attributed to the comprehensive utilization of XAI in refining parameter selection, thus leading to these significant predictive metrics. The presence of missing data in datasets is another concern; this study addresses it by employing Extreme Gradient Boosting (XGBoost) to manage missing data, effectively mitigating potential biases while preserving both the accuracy and relevance of the results. The perspective of examining data from higher dimensions using machine learning transcends traditional observation and analysis. The findings of this study hold the potential to enhance patient diagnoses and treatment, underscoring the value of merging traditional research methods with advanced analytical tools.