Post total thyroidectomy hypocalcemia: A novel multi-factorial scoring system to enable its prediction to facilitate an early discharge

Context: No single factor can predict the occurrence of post total thyroidectomy (TT) hypocalcemia. Aims: This study was conducted to look at various factors usually implicated in post TT clinically significant hypocalcemia (CSH) and to develop a scoring system using a combination of these factors to predict CSH. Settings and Design: Prospective study, tertiary care center. Materials and Methods: 145 patients, who underwent total thyroidectomy for benign goiters and early carcinoma thyroid (<T2/N0/M0), were included. Age of the patient, presence, or absence of hyperthyroidism, pre-operative levels of serum calcium and 25 OH vitamin D, postoperative iPTH at 8 hours and calcium at 12 hours, intra-operative parathyroid preservation status, and nodule size were studied. CSH prediction score (0 to 8) was designed based on these 8 factors. Statistical Analysis: SPSS 13 software was used. For comparison between groups' independent samples T-test and Chi-square test was used. Statistical significance was set at P<0.05. A logistic regression analysis model was built to assess the significant predictors. Results: There were 22 males and 123 females. 64.82% had euthyroid multinodular goiters, 24.82% had toxic MNG, and 10.34% had an early carcinoma of thyroid. 30.34% developed CSH. CSH was observed in patients with low pre-operative calcium (P=0.008), low 25 OH vitamin D (P=0.001), low post-operative iPTH at 8 hours (P=0.001), low serum calcium at 12 hours after surgery (P=0.001) and lesser number of parathyroid identification at surgery (P=0.001). Patient age (P=0.2) and nodule size (P - 0.17) was not significant. Hypocalcemia risk score of >3 had 91% sensitivity, 84% specificity with a PPV of 71% and NPV of 95%, whereas score of >= 4 had 100% specificity and PPV in predicting CSH. Conclusions: CSH after TT is multi-factorial, and a combination of factors (Hypocalcemia prediction score >3) can be used to predict it so as to discharge patients within 24 hours after surgery.