The primary aim of this study was to compare the prevalence of subclinical hypothyroidism (SCH) using two different cut-off levels for TSH values (≥2.5 mIU/L versus ≥4.1 mIU/L). The secondary objective was to analyze the clinical-biochemical characteristics in women with and without SCH. This was a retrospective cross-sectional study. In total, 1496 Mexican women with infertility were included: Group 1, women with TSH levels ranging between 0.3 and 2.49 mIU/L, n = 886; Group 2, women with TSH between 2.5 and 4.09 mIU/L, n = 390; and Group 3, women with TSH ≥4.1 mIU/L n = 220. SCH prevalence was 40.7% (CI 95%: 38.3-43.3%) with TSH cut-off ≥ 2.5 mIU/L, and 14.7% (CI 95%: 12.7-16.5%) with TSH cut-off ≥ 4.1 mIU/L, (p = 0.0001). The prevalence of overweight was higher in Group 2 than in Groups 1 and 3. Thyroid autoimmunity, obesity and insulin resistance were higher in Group 3 than in Group 1 (p < 0.05). No other differences were observed between groups. Conclusions: The prevalence of SCH in our selected patients increased almost three times using a TSH cut-off ≥ 2.5 mIU/L compared with a TSH cut-off ≥ 4.1 mIU/L. Women with TSH ≥4.1 mIU/L compared with TSH cut-off ≤ 2.5 mIU/L more often presented with obesity, thyroid autoimmunity and insulin resistance.

https://www.mdpi.com/2075-4418/11/3/417/pdf

Effect of the Cut-Off Level for Thyroid-Stimulating Hormone on the Prevalence of Subclinical Hypothyroidism among Infertile Mexican Women

Resumen La poliuria es una condicion clinica frecuente caracterizada por un volumen de orina inapropiadamente alto para los niveles de presion arterial y sodio plasmatico del paciente (volumen de orina > 3 L/24 h). Desde el punto de vista fisiopatologico se clasifica en 2 tipos: debido a una mayor excrecion de solutos (osmolaridad urinaria > 300 mOsm/L) o debido a una incapacidad de aumentar la concentracion de solutos (osmolaridad urinaria

Poliuria en el adulto. Una aproximación diagnóstica basada en la fisiopatología

Abstract Objectives Automated machine learning (AutoML) tools can help clinical laboratory professionals to develop machine learning models. The objective of this study was to develop a novel formula for the estimation of urine osmolality using an AutoML tool and to determine the efficiency of AutoML tools in a clinical laboratory setting. Methods Three hundred routine urinalysis samples were used for reference osmolality and urine clinical chemistry analysis. The H2O AutoML engine completed the machine learning development steps with minimum human intervention. Four feature groups were created, which include different urinalysis measurements according to the Boruta feature selection algorithm. Method comparison statistics including Spearman’s correlation, Passing–Bablok regression analysis were performed, and Bland Altman plots were created to compare model predictions with the reference method. The minimum allowable bias (24.17%) from biological variation data was used as the limit of agreement. Results The AutoML engine developed a total of 183 ML models. Conductivity and specific gravity had the highest variable importance. Models that include conductivity, specific gravity, and other urinalysis parameters had the highest R2 (0.70–0.83), and 70–84% of results were within the limit of agreement. Conclusions Combining urinary conductivity with other urinalysis parameters using validated machine learning models can yield a promising surrogate. Additionally, AutoML tools facilitate the machine learning development cycle and should be considered for developing ML models in clinical laboratories.

Searching for the urine osmolality surrogate: an automated machine learning approach

Polyuria is a common clinical condition characterized by a urine output that is inappropriately high (more than 3 L in 24 h) for the patient’s blood pressure and plasma sodium levels. From a pathophysiological point of view, it is classified into two types: polyuria due to a greater excretion of solutes (urine osmolality >300 mOsm/L) or due to an inability to increase solute concentration (urine osmolality

Polyuria in adults. A diagnostic approach based on pathophysiology.

Urinary iodine concentration (UIC) is routinely used to evaluate the population iodine status while the uniform method for the individual level assessment is uncertain.To explore the 24-h urinary iodine excretion (UIE) in five different periods of the day and the corresponding prediction equations respect by the use of creatinine-corrected UIC.We collected 24-h, spot and fasting urine in five periods of the day to estimate 24-h UIE by the six different prediction equations. We compared the estimated creatinine-corrected UIC to the collected 24-h UIE and identified the most suitable equations in each period of the day.Among the six different prediction equations, the equation of Kawasaki T was the best to estimate the 24-h UIE by fasting urine among Chinese adults. Among the five periods of time, the equation of Knudsen N was the best to estimate the 24-h UIE in the non-morning period.Urinary iodine status at the individual level could be estimated by different creatinine-based equations at different periods of the day. 

/pdf/is-the-urinary-iodine-creatinine-ratio-applicable-to-assess-g7imdqeh.pdf

Is the urinary iodine/creatinine ratio applicable to assess short term individual iodine status in Chinese adults? Comparison of iodine estimates from 24-h urine and timed-spot urine samples in different periods of the day

Background: Urine osmolarity (OsmU) is the gold standard for the evaluation of the kidney’s urine concentration capacity; 
nevertheless, urinary density (UD) is often used as a surrogate for its estimation. Objective: The objective of this study was 
to analyze the accuracy of UD in estimating OsmU. Materials and Methods: A transversal study including patients with simultaneous determination of UD measured with refractometry and OsmU measured by osmometer (OsmUm). We multiplied the 
last two digits of the UD by 35, 30, 32, 33.5, and 40 to estimate OsmU; the estimates were considered precise if the value 
was ± 30 mOsm/kg from the OsmUm. A Bland–Altman analysis was conducted. Results: Among 205 patients, there was no 
difference between OsmUm and the estimated form when using a factor of 33.5 (p = 0.578). When analyzing by the absence 
or presence of proteinuria and/or glycosuria, there were no differences when using the factors 35 (p = 0.844) and 32 with 
adjusted UD (p = 0.898). In the linear correlation analysis, values for Pearson’s r = 0.788 and r2 = 0.621 were obtained (p < 
0.001). The areas under the curve obtained by the receiver operating characteristics curves to estimate urine osmolarity values 600 mOsm/kg were > 0.90. Conclusion: The estimation of the OsmU from UD showed adequate perf rmance. If 
an osmometer is unavailable, we recommend using the factor 35 for clean samples and 32 with adjusted UD for samples with 
proteinuria and/or glycosuria. (REV INVEST CLIN. 2018;70:310-8)

/pdf/calculated-versus-measured-urine-osmolarity-accuracy-of-4wj3uxg352.pdf

Calculated Versus Measured Urine Osmolarity: Accuracy of Estimated Urine Density

espanolAntecedentes: varios estudios sugieren que las concentraciones perioperatorias de troponinas de alta sensibilidad son factores incrementales y predictivos de un evento cardiaco adverso mayor (MACE) y mortalidad por todas las causas. Objetivo: El objetivo del estudio fue evaluar el valor predictivo de la troponina I cardiaca de alta sensibilidad (hs-cTnI) en el desarrollo de MACE y mortalidad por todas las causas, dentro de los 30 dias y 1 ano de seguimiento despues de la cirugia no cardiaca. . Metodos: En este estudio de cohorte prospectivo, se incluyeron hombres ≥ 45 anos y mujeres ≥ 55 anos con ≥ 2 factores de riesgo cardiovascular y sometidos a cirugia no cardiaca de riesgo intermedio o alto. La informacion demografica y clinica se recopilo a partir de historias clinicas. Se midio la hs-cTnI basal 24 h antes de la cirugia y su concentracion postoperatoria 24 h despues de la cirugia. Resultados: En toda la muestra, 8 pacientes (8,6%) desarrollaron MACE a los 30 dias de seguimiento (4 muertes), 12 (12,9%) en el primer ano (7 muertes) y 17 (18,2%) despues de completar el seguimiento. -seguimiento quirurgico (10 muertes). Observamos concentraciones basales y postoperatorias mas elevadas en los pacientes que presentaban MACE (12 pg / ml frente a 3,5 pg / ml; p = 0,001 y 18,3 pg / ml frente a 5,45 pg / ml; p = 0,009, respectivamente). Los cocientes de riesgo (HR) calculados mediante el analisis de regresion de Cox entre la concentracion basal de hs-cTnI y el desarrollo posoperatorio de MACE a los 30 dias y 1 ano fueron 5,70 (intervalo de confianza [IC] del 95%, 1,10-29,40) con hs-cTnI> 6,2 pg / ml y 12,86 (IC del 95%, 1,42-116,34) con hs-cTnI> 3,3 pg / ml, respectivamente. El riesgo estimado de muerte por HR postoperatorio a 1 ano fue de 14,43 (IC del 95%, 1,37-151,61) con hs-cTnI> 4,5 pg / ml. Conclusiones: La hs-cTnI preoperatoria fue un factor de riesgo predictivo independiente para MACE a los 30 dias y 1 ano despues de la cirugia no cardiaca y para la mortalidad por todas las causas 1 ano despues de la cirugia no cardiaca. EnglishBackground: Various studies suggest that perioperative concentrations of high-sensitivity troponins are incremental and predictive factors of a major adverse cardiac event (MACE) and all-cause mortality. Objective: The objective of the study was to evaluate the predictive value of high-sensitivity cardiac troponin I (hs-cTnI) in the development of MACE and all-cause mortality, within 30-days and 1-year follow-up after noncardiac surgery. Methods: In this prospective cohort study, we in- cluded men ≥ 45 years and women ≥ 55 years with ≥ 2 cardiovascular risk factors and undergoing intermediate or high-risk noncardiac surgery. Demographic and clinical information was collected from clinical charts. We measured baseline hs-cTnI 24 h before surgery, and its post-operative concentration 24 h after surgery. Results: In the entire sample, 8 patients (8.6%) developed MACE at 30-days follow-up (4 deaths), 12 (12.9%) within the 1st year (7 deaths), and 17 (18.2%) after complete post-surgical follow-up (10 deaths). We observed higher baseline and post-operative concentrations in patients who pre- sented MACE (12 pg/ml vs. 3.5 pg/ml; p = 0.001 and 18.3 pg/ml vs. 5.45 pg/ml; p = 0.009, respectively). The hazard ratios (HRs) calculated by Cox regression analysis between the hs-cTnI baseline concentration and the post-operative development of MACE at 30-days and 1-year were 5.70 (95% confidence interval [CI], 1.10-29.40) with hs-cTnI > 6.2 pg/ml and 12.86 (95% CI, 1.42-116.34) with hs-cTnI > 3.3 pg/ml, respectively. The estimated post-operative HR death risk at 1-year was 14.43 (95% CI, 1.37-151.61) with hs-cTnI > 4.5 pg/ml. Conclusions: Pre-operative hs-cTnI was an independent predictive risk factor for MACE at 30-days and 1-year after noncardiac surgery and for all-cause mortality at 1-year after noncardiac surgery. (REV INVEST CLIN. 2020;72(2):110-8)

/pdf/evaluation-of-perioperative-high-sensitive-cardiac-troponin-vscidud2v1.pdf

Evaluation of perioperative high-sensitive cardiac troponin i as a predictive biomarker of major adverse cardiovascular events after noncardiac surgery.

The aim of this study was to evaluate the prevalence of thyroid dysfunction in a cohort of healthy individuals in Mexico City, as well as to investigate the potential associations of these results with their estimated iodine intake (EII) as reflected by their 24-hour urinary iodine excretion (24-h UIE). From the SALMEX cohort, 683 adults provided an appropriate 24-h urine sample. Thyroid function tests and thyroid antibody concentrations were determined in the participants’ sera. We analyzed discrepancies between the commonly used urinary parameters to determine the iodine intake status and the performance of thyroglobulin (Tg) as a biomarker of its status in the adult population. The prevalence of dysthyroidism was high, being similar to other studies. Subclinical hypothyroidism was detected in 5.0% of individuals, clinical hypothyroidism in 1.8% of individuals, and sub-clinical hyperthyroidism in 2.8% of individuals. The median EII was 285 μg/d (IQR 215.0–369.0); 94% of individuals had EII >150 µg/d recommended daily allowance (RDA) in adults. The urinary iodine concentration (UIC) and the UIE had relative biases in their averages of 34.4%. The Tg median was 7.21 ng/mL. The prevalence of increased Tg was 6.15%. There was no correlation between Tg and EII (r= 0.019, p= 0.606). Thyroid dysfunction was highly prevalent in this population. Our cohort revealed a slight discrepancy between dysthyroidism manifestations and iodine intake markers; the latter represent a population with adequate iodine intake. Further studies are necessary to clearly define the prevalence of thyroid dysfunction as well as the iodine nutritional status in Mexico.

Reynerio Fagundo-Sierra

Papers

Calculated Versus Measured Urine Osmolarity: Accuracy of Estimated Urine Density

Evaluation of perioperative high-sensitive cardiac troponin i as a predictive biomarker of major adverse cardiovascular events after noncardiac surgery.

Prevalence of thyroid dysfunction in healthy adults according to the estimated iodine intake in 24-hour urine samples: The SALMEX cohort.