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Development and validation of a multivariate predictive model to estimate serum ionized calcium concentration from serum biochemical profile results in cats

机译:建立和验证用于从猫的血清生化特征结果估算血清离子钙浓度的多元预测模型中

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Background Measurement of serum ionized calcium is not always available in practice. Total calcium (tCa) might not be reliable for determination of calcium status in cats. Objectives To predict serum ionized calcium concentration from signalment, biochemistry profile and T4, and compare predicted ionized calcium (piCa) to tCa. Animals A total of 1701 cats from two hospitals. Methods Cross‐sectional study. Cats with serum ionized calcium, biochemistry profile and T4 available were screened over 6?years and included in the training set (569 cats) to create a multivariate adaptive regression splines model to calculate piCa. Diagnostic performances of tCa and piCa and its prediction interval (PI) were compared in 652 cats from the same institution (test set) and 480 cats from a different hospital (external set). Results The final model included tCa, chloride, albumin, cholesterol, creatinine, BUN, body condition score, GGT, age, and potassium. For hypercalcemia, piCa was highly specific (test set: 99.8%; confidence interval [CI]: 99.5‐100; external set: 97%; CI: 95.3‐98.7) but poorly sensitive (test set: 30.4%; CI: 18.3‐42.4; external set: 42.5%; CI: 31.7‐53.3). For hypocalcemia, piCa was also highly specific (test set: 81.6%; CI: 78‐85; external set: 99.6%; CI: 99‐100) but poorly sensitive (test set: 57.6%; CI: 50.6‐64.6; external set: 0%). These diagnostic performances were comparable to those of tCa. The upper and lower limits of piCa PI had high sensitivity for detecting ionized hypercalcemia and hypocalcemia, respectively. Conclusions and clinical importance Predicted ionized calcium is useful to confirm suspected hypercalcemia in cats and screen for hypercalcemia and hypocalcemia.
机译:背景技术实际上,血清离子钙的测量并非始终可用。总钙(tCa)可能无法确定猫的钙状态。目的根据信号,生化特征和T4预测血清离子钙浓度,并将预测的离子钙(piCa)与tCa进行比较。动物两家医院共有1701只猫。方法横断面研究。经过6年的筛选,筛选出具有血清离子钙,生化特征和T4的猫,并将其包括在训练集中(569只猫),以创建多元自适应回归样条模型来计算piCa。比较了来自同一机构(测试组)的652只猫和来自不同医院(外部机构)的480只猫的tCa和piCa的诊断性能及其预测间隔(PI)。结果最终模型包括tCa,氯化物,白蛋白,胆固醇,肌酐,BUN,身体状况评分,GGT,年龄和钾。对于高钙血症,piCa具有高度特异性(测试集:99.8%;置信区间[CI]:99.5-100;外部集合:97%; CI:95.3-98.7))但敏感性较差(测试集:30.4%; CI:18.3- 42.4;外部组合:42.5%; CI:31.7-53.3)。对于低钙血症,piCa也是高度特异性的(测试组:81.6%; CI:78-85;外部组:99.6%; CI:99-100))但敏感性较差(测试组:57.6%; CI:50.6-64.6;外部)设定:0%)。这些诊断性能与tCa相当。 piCa PI的上限和下限分别对离子化高钙血症和低钙血症的检测具有较高的灵敏度。结论和临床重要性预测的离子钙可用于确认猫中的怀疑高钙血症和筛查高钙血症和低钙血症。

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