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RESUMEN Objetivo: evaluar la sensibilidad y especificidad del test G8 en el tamizaje de adultos mayores con cáncer para la realización de la valoración geriátrica integral (VGI). Materiales y métodos: el presente estudio observacional y retrospectivo se realizó en el Servicio de Geriatría del Hospital Almenara de Lima, Perú. Se revisaron los informes de VGI en las historias clínicas electrónicos de adultos mayores (> 60 años) con cáncer, ambulatorios y hospitalizados, durante noviembre de 2022 y julio de 2023. Los pacientes se clasificaron según los criterios SIOG-1 (Sociedad Internacional de Oncología Geriátrica), formando dos grupos: pacientes aptos y pacientes no aptos o unfit (vulnerables + frágiles + muy enfermos). Del test G8 se estimó la sensibilidad, especificidad y valor predictivo positivo, área bajo la curva característica operativa del receptor (AUC). Resultados: ingresaron al estudio 201 pacientes, 91 mujeres (45,3%) y 110 (54,7%) varones, la media de la edad fue de 76,2 ± 7,4 años. Las neoplasias más frecuentes fueron colorrectal, estómago, próstata y vías biliares. La prevalencia de pacientes aptos y no aptos (unfit) fue del 23,4 y 76,6%, respectivamente. Cuando el puntaje de la prueba G8 fue ≤11, la sensibilidad, especificidad, valor predictivo positivo y AUC fueron 73,4% (intervalo de confianza al 95%: 65,7-80,2%), 91,5% (79,6%-97,6%), 96,6% (91,7-98,6%) y 89% (84-93%), respectivamente. Conclusiones: el test G8 con puntaje ≤11 tendría una alta sensibilidad y especificidad, para identificar adultos con cáncer vulnerables o frágiles, que podrían beneficiarse de la VGI.

ABSTRACT Objective: To evaluate sensitivity and specificity of the G8 test in screening older adults with cancer who may benefit from a Comprehensive Geriatric Assessment (CGA). Material and methods: This observational retrospective study was carried out in the Geriatrics Service of the Guillermo Almenara Hospital in Lima, Peru. CGA reports were reviewed in the electronic medical records of older adults (> 60 years) with cancer, both outpatients and inpatients, between November 2022 and July 2023. Patients were classified according to the SIOG-1 (International Society of Geriatric Oncology) criteria into two groups: fit and non-fit patients (vulnerable + frail + too sick). Sensitivity, specificity, and positive predictive value, area under the receiver operating characteristic curve (AUC), were estimated for the G8 test. Results: 201 patients entered the study, 91 women (45.3%) and 110 (54.7%) men; their mean age was 76.2 ± 7.4 years. The most frequent neoplasms were colorectal, stomach, prostate, and bile ducts. The prevalence of eligible and unfit patients was 23.4% and 76.6%, respectively. When the G8 test score was ≤11, sensitivity, specificity, positive predictive value, and AUC were 73.4% (95% Confidence Interval: 65.7- 80.2%), 91.5% (79.6%-97.6%), 96.6% (91.7-98.6%), and 89% (84-93%), respectively. Conclusions: The G8 test with a score ≤11 would have high sensitivity and specificity for identifying vulnerable or frail patients with cancer who could benefit from the CGA.

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Abstract Background Shock index (SI) and age shock index (ASI) are less frequently used for assessment of major adverse cardiovascular events (MACE) among patients with ST-segment elevation myocardial infarction (STEMI), and their reported cut-off points are controversial. Objectives We aimed to define proper cut-off value of these indices for MACE prediction among Iranian patients with STEMI. Methods This study was in the context of the ST-Elevation Myocardial Infarction Cohort in Isfahan (SEMI-CI) study. SI and ASI were calculated by division of heart rate (HR) over systolic blood pressure (SBP) and age multiplied by SI, respectively, in 818 subjects with STEMI. Receiver operating characteristic (ROC) curve analysis was used to determine optimal SI and ASI cut-off values. Chi-square test, independent t test, and analysis of variance were employed for nominal and numerical variables, as appropriate, with consideration of p values < 0.05. MACE was defined as a composite of non-fatal reinfarction, heart failure (HF), recurrent percutaneous intervention (PCI), rehospitalization for cardiovascular diseases, and all-cause mortality. Results Mean age was 60.70 ± 12.79 years (males: 81.7%). Area under curve (AUC) values from ROC curve analysis for SI and ASI were 0.613 (95% confidence interval [CI]: 0.569 to 0.657, p < 0.001) and 0.672 (95% CI: 0.629 to 0.715, p < 0.001), respectively. Optimal SI and ASI cut-offs were 0.61 (sensitivity: 61%, specificity: 56%) and 39.5 (sensitivity: 65%, specificity: 66%), respectively. Individuals with SI ≥ 0.61 or within the highest quartile (SI ≥ 0.75) had significantly higher frequency of one-year MACE compared to the reference group (34.7% versus 22.2%, p < 0.001 and 42.4% versus 20.6%, p < 0.05, respectively). Similar relations were observed in terms of ASI values (ASI ≥ 39.5 versus ASI < 39.5: 43.6% versus 17.3%, p < 0.001, ASI Q4 ≥ 47.5 versus ASI Q1 ≤ 28.8: 49% versus 16.6%, p < 0.05). Conclusions SI and ASI cut-off values of 0.61 and 39.5 could reliably predict MACE occurrence among Iranian patients with STEMI.

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ABSTRACT BACKGROUND: Neck circumference (NC) is a useful anthropometric measure for predicting obstructive sleep apnea (OSA). Ethnicity and sex also influence obesity phenotypes. NC cut-offs for defining OSA have not been established for the Latin American population. OBJECTIVES: To evaluate NC, waist circumference (WC), and body mass index (BMI) as predictors of OSA in the Colombian population and to determine optimal cut-off points. DESIGN AND SETTING: Diagnostic tests were conducted at the Javeriana University, Bogota. METHODS: Adults from three cities in Colombia were included. NC, WC, and BMI were measured, and a polysomnogram provided the reference standard. The discrimination capacity and best cut-off points for diagnosing OSA were calculated. RESULTS: 964 patients were included (57.7% men; median age, 58 years) and 43.4% had OSA. The discrimination capacity of NC was similar for men and women (area under curve, AUC 0.63 versus 0.66, P = 0.39) but better for women under 60 years old (AUC 0.69 versus 0.57, P < 0.05). WC had better discrimination capacity for women (AUC 0.69 versus 0.57, P < 0.001). There were no significant differences in BMI. Optimal NC cut-off points were 36.5 cm for women (sensitivity [S]: 71.7%, specificity [E]: 55.3%) and 41 cm for men (S: 56%, E: 62%); and for WC, 97 cm for women (S: 65%, E: 69%) and 99 cm for men (S: 53%, E: 58%). CONCLUSIONS: NC and WC have moderate discrimination capacities for diagnosing OSA. The cut-off values suggest differences between Latin- and North American as well as Asian populations.

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The use of diagnostic tests to determine the presence or absence of a disease is essential in clinical practice. The results of a diagnostic test may correspond to numerical estimates that require quantitative reference parameters to be transferred to a dichotomous interpretation as normal or abnormal and thus implement actions for the care of a condition or disease. For example, in the diagnosis of anemia it is necessary to define a cut-off point for the hemoglobin variable and create two categories that distinguish the presence or absence of anemia. The method used for this process is the preparation of diagnostic performance curves, better known by their acronym in English as ROC (Receiver Operating Characteristic). The ROC curve is also useful as a prognostic marker, since it allows defining the cut-off point of a quantitative variable that is associated with greater mortality or risk of complications. They have been used in different prognostic markers in COVID-19, such as the neutrophil/lymphocyte ratio and D-dimer, in which cut-off points associated with mortality and/or risk of mechanical ventilation were identified. The ROC curve is used to evaluate the diagnostic performance of a test in isolation, but it can also be used to compare the performance of two or more diagnostic tests and define which one is more accurate. This article describes the basic concepts for the use and interpretation of the ROC curve, the interpretation of an area under the curve (AUC) and the comparison of two or more diagnostic tests.

El uso de pruebas diagnósticas para determinar la presencia o ausencia de una enfermedad es esencial en la práctica clínica. Los resultados de una prueba diagnóstica pueden corresponder a estimaciones numéricas que requieren parámetros cuantitativos de referencia para trasladarse a una interpretación dicotómica como normal o anormal y así, implementar acciones para la atención de una condición o una enfermedad. Por ejemplo, en el diagnóstico de anemia es necesario definir un punto de corte para la variable hemoglobina y crear dos categorías que distingan la presencia o no de anemia. El método utilizado para este proceso es la elaboración de curvas de rendimiento diagnóstico, mejor conocidas por sus siglas en inglés como ROC (Receiver Operating Characteristic). La curva ROC además es útil como marcador pronóstico, ya que permite definir el punto de corte de una variable cuantitativa que se asocia a mayor mortalidad o riesgo de complicaciones. Se han usado en distintos marcadores pronósticos en COVID-19, como el índice neutrófilos/linfocitos y dímero D, en los que se identificaron puntos de corte asociados a mortalidad y/o riesgo de ventilación mecánica. La curva ROC se utiliza para evaluar el rendimiento diagnóstico de una prueba de forma aislada, pero también se puede usar para comparar el rendimiento de dos o más pruebas diagnósticas y definir aquella que es más precisa. En este artículo se describen los conceptos básicos para el uso e interpretación de la curva ROC, la interpretación de un área bajo la curva (ABC) y la comparación de dos o más pruebas diagnósticas.

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OBJECTIVE: In this study, we aimed to evaluate the factors affecting major adverse event (MAE) development after full-term neonatal cardiac surgery. METHODS: This study was conducted retrospectively on newborns who underwent congenital heart surgery between June 1, 2020, and June 1, 2022. MAE was defined as the presence of at least one of the following: cardiac arrest, unplanned reoperation, emergency chest opening, admission to the advanced life support system, and death. The role of blood lactate level, vasoactive inotropic score (VIS), and cerebral near-infrared spectroscopy (NIRS) changes in predicting MAE was investigated. RESULTS: A total of 240 patients (50% male) were operated during the study period. The median age of patients was seven days (interquartile range 3-10 days). MAE was detected in 19.5% of the cases. Peak blood lactate levels >7 mmol/liter (area under the curve [AUC] 0.72, 95% confidence interval [CI] [0.62-0.82], P<0.001, sensitivity 76%, specificity 82%, positive predictive value [PPV] 88%) was an independent risk factor for MAE (odds ratio [OR] 2.7 [95% CI 1.3-6]). More than 30% change in NIRS value during the operative period (AUC 0.84, 95% CI [0.80-0.88], P<0.001, sensitivity 65%, specificity 85%, PPV 90%) was a strong predictor of MAE. VIS > 10 was an independent risk factor (AUC 0.75, 95% CI [0.70-0.84], P<0.001, sensitivity 86%, specificity 80%, PPV 84%) and strongly predicted MAE (OR 1.4 [95% CI 0.9-5]). CONCLUSION: Cerebral NIRS changes > 30%, high blood lactate levels, and VIS score within the 48 hours may help to predict the development of MAE in the postoperative period.

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INTRODUCTION: The European System for Cardiac Operative Risk Evaluation (EuroSCORE) II and the Society of Thoracic Surgeons (STS) are validated scoring systems for short-term risk estimation after coronary artery bypass grafting (CABG). The Meta-Analysis Global Group in Chronic Heart Failure (MAGGIC) risk score is originally aimed to estimate mortality in heart failure patients; however, it has showed a similar power to predict mortality after heart valve surgery. In this study, we sought to evaluate whether MAGGIC score may predict short and long-term mortality after CABG and to compare its power with EuroSCORE II and STS scoring systems. METHODS: Patients who underwent CABG due to chronic coronary syndrome at our institution were included in this retrospective study. Follow-up data were used to define the predictive ability of MAGGIC and to compare it with STS and EuroSCORE-II for early, one-year, and up to 10-year mortality. RESULTS: MAGGIC, STS, and EuroSCORE-II scores had good prognostic power, moreover MAGGIC was better for predicting 30-day (area under the curve [AUC]: 0.903; 95% confidence interval [CI]: 0.871-0.935), one-year (AUC: 0.931; 95% CI: 0.907-0.955), and 10-year (AUC: 0.923; 95% CI: 0.893-0.954) mortality. MAGGIC was found to be an independent predictor to sustain statistically significant association with mortality in follow-up. CONCLUSION: MAGGIC scoring system had a good predictive accuracy for early and long-term mortality in patients undergoing CABG when compared to EuroSCORE-II and STS scores. It requires limited variables for calculation and still yields better prognostic power in determining 30-day, one-year, and up to 10-year mortality.

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Establecer la capacidad discriminativa del puntaje de riesgo finlandés para disglucemia en usuarios de una unidad de medicina familiar localizada en zona conurbana del Estado de Guerrero, México. Material y métodos: Realizamos un estudio transversal de marzo a diciembre del 2021 en una Unidad de Medicina Familiar. Previo consentimiento informado aplicamos a 200 personas de 20 a 60 años, el puntaje de riesgo finlandés para detección de disglucemia, obtuvimos medidas somatométricas y cifras de glucosa plasmática en ayuno. Estimamos sensibilidad, especificidad, valor predictivo positivo y negativo, razón de verosimilitud positiva y negativa, y calculamos el área bajo la curva (AUC) para estimar la capacidad discriminativa del puntaje de riesgo, donde la prueba de referencia fue la glucosa en ayuno. Realizamos análisis bivariado para identificar factores asociados a disglucemia, obteniendo Odds Ratio (OR), e intervalos de confianza del 95 % (IC95%). La ocurrencia de disglucemia fue de 26.5 % (53/200). El AUC de la curva ROC del puntaje finlandés para disglucemia fue de 0.65 (IC95% 0.57-0.74). Los factores asociados a diabetes fueron ≥40 años (OR 2.1; IC95% 1.1-3.9), índice de masa corporal ≥25 Kg/m2 (OR 2.8; IC95% 1.2-6.7) y padecer hipertensión arterial (OR 2.2; IC95% 1.1-4.4). El FINDRISC demostró por AUC ser una mala herramienta para detectar personas en riesgo de padecer disglucemia, en población adscrita a unidad médica conurbana.

To establish the discriminative capacity of the Finnish risk score for dysglycemia in users of a family medicine unit located in the suburbs of the State of Guerrero, Mexico. We conducted a cross-sectional study from March to December 2021 in a Family Medicine Unit. With prior informed consent, we applied the Finnish risk score for the detection of dysglycemia to 200 people between the ages of 20 and 60, we obtained somatometric measurements and fasting plasma glucose figures. We estimated sensitivity, specificity, positive and negative predictive value, positive and negative likelihood ratio, and calculated the area under the curve (AUC) to estimate the discriminative ability of the risk score, where the reference test was fasting glucose. We performed bivariate analysis to identify factors associated with dysglycemia, obtaining Odds Ratio (OR) and 95% confidence intervals (95%CI). Result: The occurrence of dysglycemia was 26.5% (53/200). The AUC of the ROC curve of the Finnish score for dysglycemia was 0.65 (95%CI 0.57-0.74). The factors associated with diabetes were ≥40 years (OR 2.1; 95%CI 1.1-3.9), body mass index ≥25 Kg/m2 (OR 2.8; 95%CI 1.2-6.7) and suffering from arterial hypertension (OR 2.2; 95%CI 1.1 -4.4). The FINDRISC was shown by AUC to be a poor tool for detecting people at risk of suffering from dysglycemia, in a population attached to a suburban medical unit

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Introducción: La presencia del síndrome metabólico está asociada con enfermedades crónicas a largo plazo, por lo que se buscan diferentes formas de obtener un diagnóstico temprano. Objetivo: Determinar el rendimiento diagnóstico de 3 índices antropométricos de peso y talla para síndrome metabólico en una muestra de trabajadores peruanos. Métodos: La población son trabajadores de 18 a 65 años, de ambos sexos, ocupación operarios y administrativos. Las variables estudiadas son: edad, sexo, ocupación, peso, talla, perímetro de cintura, antecedentes de diabetes mellitus tipo 2, presión arterial sistólica, diastólica, glucosa en ayunas, triglicéridos y lipoproteína de alta densidad. Se incluyeron 370 trabajadores, se crearon curvas características operativa del receptor con su respectiva área bajo la curva (AUC), se obtuvo la sensibilidad y especificidad de cada índice. Resultados: Del total, el 20 % presentó síndrome metabólico; el 46,76 % fueron mujeres, el 60 % tomaron alcohol alguna vez, el 5,14 % señaló haber fumado. El índice de masa corporal tuvo la mayor AUC= 0,73; corte= 26,04; sensibilidad= 78,4 y especifidad= 67,9) seguido del nuevo índice de masa corporal (AUC= 0,70; corte= 27,85; sensibilidad= 68,9 y especificidad= 70,6); el último lugar lo ocupa el índice triponderal (AUC= 0,66; corte= 16,67; sensibilidad= 67,6 y especificidad= 64,5); los parámetros para síndrome metabólico mostraron asociación estadísticamente significativa. Conclusión: El índice de masa corporal es el de mejor rendimiento diagnóstico para síndrome metabólico; podría ser un predictor útil para detectar este síndrome.

Introduction: Metabolic syndrome is associated with long-term chronic diseases, which is why different ways of obtaining an early diagnosis are sought. Objective: To determine the diagnostic yield of 3 anthropometric indices of weight and height for metabolic syndrome in a sample of Peruvian workers. Methods: The population are workers from 18 to 65 years old, both sexes, occupation operators and administrators; the studied variables were: age, sex, occupation, weight, height, waist circumference, history of type 2 diabetes mellitus, pressure systolic and diastolic blood pressure, fasting glucose, triglycerides, and high-density lipoprotein; 370 workers were included, receiver operating characteristic curves (ROC) were created with their respective area under the curve, obtaining the sensitivity and specificity of each of the indices. Results: Of the total number of workers, 20% presented Metabolic Syndrome; 46.76% were women, 60% drank alcohol at some time, and 5.14% reported having smoked. The Body Mass Index the greatest ROC= 0.73; cutoff= 26.04; sensitivity= 78.4 and specificity= 67.9) followed by the New Body Mass Index (ROC= 0.70; cutoff= 27.85; sensitivity= 68.9 and specificity= 70.6), the last place was occupied by the Triponderal Index (ROC= 0.66; cutoff= 16.67; sensitivity= 67.6 and specificity= 64.5); the parameters for metabolic syndrome showed a statistically significant association. Conclusion: Body Mass Index is the best diagnostic yield for Metabolic Syndrome and could be a useful predictor to detect this syndrome.

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INTRODUCTION: Transfusion of red blood cells is recurrent in cardiac surgery despite the well-established deleterious effects. Identifying patients with higher chances of requiring blood transfusion is essential to apply strategic preventive measures to reduce such chances, considering the restricted availability of this product. The most used risk scores to predict blood transfusion are the Transfusion Risk and Clinical Knowledge (TRACK) and Transfusion Risk Understanding Scoring Tool (TRUST). However, these scores were not validated for the Brazilian population. The objective of this study was to assess the accuracy of TRACK and TRUST scores in estimating the need for postoperative transfusion of red blood cell concentrates (TRBCC) after cardiac surgery. METHODS: A clinical retrospective study was conducted using the database of a Brazilian reference service composed of patients operated between November 2019 and September 2021. Scores were compared using Mann-Whitney U test. Hosmer-Lemeshow goodness of fit test assessed calibration of the scores. Accuracy was assessed using the area under the receiver operating characteristic curve (AUC). All analyses considered a level of significance of 5%. The study was approved by the research ethics committee (CAAE 55577421.4.0000.5201). RESULTS: This study assessed 498 patients. Only the TRACK score presented good calibration (P=0.238; TRUST P=0.034). AUC of TRACK was 0.678 (95% confidence interval 0.63 to 0.73; P<0.001), showing a significant accuracy. CONCLUSION: Between the scores analyzed, only the TRACK score showed a good calibration, but low accuracy, to predict postoperative TRBCC after cardiac surgery.

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ABSTRACT Introduction: The European System for Cardiac Operative Risk Evaluation (EuroSCORE) II and the Society of Thoracic Surgeons (STS) are validated scoring systems for short-term risk estimation after coronary artery bypass grafting (CABG). The Meta-Analysis Global Group in Chronic Heart Failure (MAGGIC) risk score is originally aimed to estimate mortality in heart failure patients; however, it has showed a similar power to predict mortality after heart valve surgery. In this study, we sought to evaluate whether MAGGIC score may predict short and long-term mortality after CABG and to compare its power with EuroSCORE II and STS scoring systems. Methods: Patients who underwent CABG due to chronic coronary syndrome at our institution were included in this retrospective study. Follow-up data were used to define the predictive ability of MAGGIC and to compare it with STS and EuroSCORE-II for early, one-year, and up to 10-year mortality. Results: MAGGIC, STS, and EuroSCORE-II scores had good prognostic power, moreover MAGGIC was better for predicting 30-day (area under the curve [AUC]: 0.903; 95% confidence interval [CI]: 0.871-0.935), one-year (AUC: 0.931; 95% CI: 0.907-0.955), and 10-year (AUC: 0.923; 95% CI: 0.893-0.954) mortality. MAGGIC was found to be an independent predictor to sustain statistically significant association with mortality in follow-up. Conclusion: MAGGIC scoring system had a good predictive accuracy for early and long-term mortality in patients undergoing CABG when compared to EuroSCORE-II and STS scores. It requires limited variables for calculation and still yields better prognostic power in determining 30-day, one-year, and up to 10-year mortality.