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Clinical data from hospital admissions are typically utilized to determine the prognostic capacity of Coronavirus disease 2019 (COVID-19) indices. However, as disease status and severity markers evolve over time, time-dependent receiver operating characteristic (ROC) curve analysis becomes more appropriate. The present analysis assessed predictive power for death at various time points throughout patient hospitalization. In a cohort study involving 515 hospitalized patients (General Hospital Number 1 of Mexican Social Security Institute, Colima, Mexico from February 2021 to December 2022) with COVID-19, seven severity indices [Pneumonia Severity Index (PSI) PaO2/FiO2 arterial oxygen pressure/fraction of inspired oxygen (Kirby index), the Critical Illness Risk Score (COVID-GRAM), the National Early Warning Score 2 (NEWS-2), the quick Sequential Organ Failure Assessment score (qSOFA), the Fibrosis-4 index (FIB-4) and the Viral Pneumonia Mortality Score (MuLBSTA were evaluated using time-dependent ROC curves. Clinical data were collected at admission and at 2, 4, 6 and 8 days into hospitalization. The study calculated the area under the curve (AUC), sensitivity, specificity, and predictive values for each index at these time points. Mortality was 43.9%. Throughout all time points, NEWS-2 demonstrated the highest predictive power for mortality, as indicated by its AUC values. PSI and COVID-GRAM followed, with predictive power increasing as hospitalization duration progressed. Additionally, NEWS-2 exhibited the highest sensitivity (>96% in all periods) but showed low specificity, which increased from 22.9% at admission to 58.1% by day 8. PSI displayed good predictive capacity from admission to day 6 and excellent predictive power at day 8 and its sensitivity remained >80% throughout all periods, with moderate specificity (70.6-77.3%). COVID-GRAM demonstrated good predictive capacity across all periods, with high sensitivity (84.2-87.3%) but low-to-moderate specificity (61.5-67.6%). The qSOFA index initially had poor predictive power upon admission but improved after 4 days. FIB-4 had a statistically significant predictive capacity in all periods (P=0.001), but with limited clinical value (AUC, 0.639-0.698), and with low sensitivity and specificity. MuLBSTA and IKIRBY exhibited low predictive power at admission and no power after 6 days. In conclusion, in COVID-19 patients with high mortality rates, NEWS-2 and PSI consistently exhibited predictive power for death during hospital stay, with PSI demonstrating the best balance between sensitivity and specificity.

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Paracoccidioidomycosis (PCM) is a systemic mycosis that is diagnosed by visualizing the fungus in clinical samples or by other methods, like serological techniques. However, all PCM diagnostic methods have limitations. The aim of this study was to develop a diagnostic tool for PCM based on Fourier transform infrared (FTIR) spectroscopy. A total of 224 serum samples were included: 132 from PCM patients and 92 constituting the control group (50 from healthy blood donors and 42 from patients with other systemic mycoses). Samples were analyzed by attenuated total reflection (ATR) and a t-test was performed to find differences in the spectra of the two groups. The wavenumbers that had p < 0.05 had their diagnostic potential evaluated using receiver operating characteristic (ROC) curves. The spectral region with the lowest p value was used for variable selection through principal component analysis (PCA). The selected variables were used in a linear discriminant analysis (LDA). In univariate analysis, the ROC curves with the best performance were obtained in the region 1551-1095 cm-1. The wavenumber that had the highest AUC value was 1264 cm-1, achieving a sensitivity of 97.73%, specificity of 76.01%, and accuracy of 94.22%. The total separation of groups was obtained in the PCA performed with a spectral range of 1551-1095 cm-1. LDA performed with the eight wavenumbers with the greatest weight from the group discrimination in the PCA obtained 100% accuracy. The methodology proposed here is simple, fast, and highly accurate, proving its potential to be applied in the diagnosis of PCM. The proposed method is more accurate than the currently known diagnostic methods, which is particularly relevant for a neglected tropical mycosis such as paracoccidioidomycosis.

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PURPOSE: To investigate the validity of the Physical Activity Questionnaire for Older Children (PAQ-C) to assess the moderate- to vigorous-intensity physical activity (MVPA) level of children and adolescents diagnosed with HIV and propose cut-points, with accelerometer measures as the reference method. METHOD: Children and adolescents, aged 8-14 years (mean age = 12.21 y, SD = 2.09), diagnosed with HIV by vertical transmission, participated in the study. MVPA was investigated through the PAQ-C and triaxial accelerometer (ActiGraph GT3X+). Receiver operating characteristic curve and sensitivity and specificity values were used to identify a cut-point for PAQ-C to distinguish participants meeting MVPA guidelines. RESULTS: Fifty-six children and adolescents participated in the study. Among those, 16 met MVPA guidelines. The PAQ-C score was significantly related to accelerometry-derived MVPA (ρ = .506, P < .001). The PAQ-C score cut-point of 2.151 (sensitivity = 0.625, specificity = 0.875) was able to discriminate between those who met MVPA guidelines and those that did not (area under the curve = 0.751, 95% confidence interval, 0.616-0.886). CONCLUSION: The PAQ-C was useful to investigate MVPA among children and adolescents diagnosed with HIV and to identify those who meet MVPA guidelines.

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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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Background: The use of instruments in clinical practice with measurement properties tested is highly recommended, in order to provide adequate assessment and measurement of outcomes. Objective: To calculate the minimum clinically important difference (MCID) and responsiveness of the Perme Intensive Care Unit Mobility Score (Perme Score). Methods: This retrospective, multicentric study investigated the clinimetric properties of MCID, estimated by constructing the Receiver Operating Characteristic (ROC). Maximizing sensitivity and specificity by Youden's, the ROC curve calibration was performed by the Hosmer and Lemeshow goodness-of-fit test. Additionally, we established the responsiveness, floor and ceiling effects, internal consistency, and predictive validity of the Perme Score. Results: A total of 1.200 adult patients records from four mixed general intensive care units (ICUs) were included. To analyze which difference clinically reflects a relevant evolution we calculated the area under the curve (AUC) of 0.96 (95% CI: 0.95-0.98), and the optimal cut-off value of 7.0 points was established. No substantial floor (8.8%) or ceiling effects (4.9%) were observed at ICU discharge. However, a moderate floor effect was observed at ICU admission (19.3%), in contrast to a very low incidence of ceiling effect (0.6%). The Perme Score at ICU admission was associated with hospital mortality, OR 0.86 (95% CI: 0.82-0.91), and the predictive validity for ICU stay presented a mean ratio of 0.97 (95% CI: 0.96-0.98). Conclusion: Our findings support the establishment of the minimum clinically important difference and responsiveness of the Perme Score as a measure of mobility status in the ICU.

Antecedentes: Se recomienda encarecidamente el uso de instrumentos en la práctica clínica con propiedades de medición probadas, con el fin de proporcionar una evaluación y medición adecuada de los resultados. Objetivo: Calcular la diferencia mínima clínicamente importante (MCID) y la capacidad de respuesta de la puntuación de movilidad de la unidad de cuidados intensivos de Perme (Perme Score). Métodos: Este estudio multicéntrico retrospectivo investigó las propiedades clinimétricas de MCID, estimadas mediante la construcción de la característica operativa del receptor (ROC). Maximizando la sensibilidad y especificidad mediante la prueba de Youden, la calibración de la curva ROC se realizó mediante la prueba de bondad de ajuste de Hosmer y Lemeshow. Además, establecimos la capacidad de respuesta, los efectos suelo y techo, la consistencia interna y la validez predictiva del Perme Score. Resultados: Se incluyeron un total de 1,200 registros de pacientes adultos de cuatro unidades de cuidados intensivos (UCI) generales mixtas. Para analizar qué diferencia refleja clínicamente una evolución relevante calculamos el área bajo la curva (AUC) de 0.96 (95% CI: 0.95-0.98); y se estableció el valor de corte óptimo de 7.0 puntos. No se observaron efectos suelo (8.8%) o techo (4.9%) sustanciales al alta de la UCI. Sin embargo, se observó un efecto suelo moderado al ingreso en la UCI (19.3%), en contraste con una incidencia muy baja del efecto techo (0.6%). El Perme Score al ingreso en UCI se asoció con la mortalidad hospitalaria, OR 0.86 (95% CI: 0.82-0.91), y la validez predictiva de estancia en UCI presentó una relación media de 0.97 (95% CI: 0.96-0.98). Conclusiones: Nuestros hallazgos respaldan el establecimiento de la diferencia mínima clínicamente importante y la capacidad de respuesta de el Perme Score como medida del estado de movilidad en la UCI.

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The escalating prevalence of overall and abdominal obesity, particularly affecting Latin America, underscores the urgent need for accessible and cost-effective predictive methods to address the growing disease burden. This study assessed skinfold thicknesses' predictive capacity for overall and abdominal obesity in Peruvian adults aged 30 or older over 5 years. Data from the PERU MIGRANT 5-year cohort study were analyzed, defining obesity using BMI and waist circumference. Receiver operating characteristic curves and area under the curve (AUC) with 95% confidence intervals (CI) were calculated. Adults aged ≥ 30 (n = 988) completed the study at baseline, with 47% male. A total of 682 participants were included for overall and abdominal obesity analysis. The 5-year prevalence values for overall and abdominal obesity were 26.7% and 26.6%, respectively. Subscapular skinfold (SS) best predicted overall obesity in men (AUC = 0.81, 95% CI: 0.75-0.88) and women (AUC = 0.77, 95% CI: 0.67-0.88). Regarding abdominal obesity, SS exhibited the highest AUC in men (AUC = 0.83, 95% CI: 0.77-0.89), while SS and the sum of trunk skinfolds showed the highest AUC in women. In secondary analysis excluding participants with type-2 diabetes mellitus (DM2) at baseline, SS significantly predicted DM2 development in men (AUC = 0.70, 95% CI: 0.58-0.83) and bicipital skinfold (BS) did in women (AUC = 0.73, 95% CI: 0.62-0.84). The findings highlight SS significance as an indicator of overall and abdominal obesity in both sexes among Peruvian adults. Additionally, SS, and BS offer robust predictive indicators for DM2.

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BACKGROUND: Little is known about the performance of severity indices for indicating intensive care and predicting mortality in the Intensive Care Unit (ICU) of trauma patients. This study aimed to compare the performance of severity indices to predict trauma patients' ICU admission and mortality. METHODS: A retrospective cohort study which analyzed the electronic medical records of trauma patients aged ≥ 18 years, treated at a hospital in Brazil, between 2014 and 2017. Physiological [Revised Trauma Score (RTS), New Trauma Score (NTS) and modified Rapid Emergency Medicine Score (mREMS)], anatomical [Injury Severity Score (ISS) and New Injury Severity Score (NISS)] and mixed indices [Trauma and Injury Severity Score (TRISS), New Trauma and Injury Severity Score (NTRISS), Base-deficit Injury Severity Score (BISS) and Base-deficit and New Injury Severity Score (BNISS)] were compared in analyzing the outcomes (ICU admission and mortality) using the Area Under the Receiver Operating Characteristics Curves (AUC-ROC). RESULTS: From the 747 trauma patients analyzed (52.5% female; mean age 51.5 years; 36.1% falls), 106 (14.2%) were admitted to the ICU and 6 (0.8%) died in the unit. The ISS (AUC 0.919) and NISS (AUC 0.916) had better predictive capacity for ICU admission of trauma patients. The NISS (AUC 0.949), TRISS (AUC 0.909), NTRISS (AUC 0.967), BISS (AUC 0.902) and BNISS (AUC 0.976) showed excellent performance in predicting ICU mortality. CONCLUSIONS: Anatomical indices showed excellent predictive ability for admission of trauma patients to the ICU. The NISS and the mixed indices had the best performances regarding mortality in the ICU.

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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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The study aimed to identify accurate cut-off points for waist circumference (WC), body fat percentage (BF%), body mass index (BMI), fat mass index (FMI), and fat-free mass index (FFMI), and to determine their effective accuracy to predict cardiovascular risk factors (CVRFs) among Mexican young adults. A cross-sectional study was conducted among 1730 Mexican young adults. Adiposity measures and CVRFs were assessed under fasting conditions. The optimal cut-off points were assessed using the receiver operating characteristic curve (ROC). Age-adjusted odds ratios (OR) were used to assess the associations between anthropometric measurements and CVRFs. The cut-off values found, in females and males, respectively, for high WC (≥72.3 and ≥84.9), high BF% (≥30 and ≥22.6), high BMI (≥23.7 and ≥24.4), high FMI (≥7.1 and ≥5.5), and low FFMI (≤16 and ≤18.9) differ from those set by current guidelines. High BMI in women, and high FMI in men, assessed by the 50th percentile, had the best discriminatory power in detecting CVRFs, especially high triglycerides (OR: 3.07, CI: 2.21-4.27 and OR: 3.05, CI: 2.28-4.08, respectively). Therefore, these results suggest that BMI and FMI measures should be used to improve the screening of CVRFs in Mexican young adults.

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BACKGROUND: In 2020, Ecuador had one of the highest death rates because of COVID-19. The role of clinical and biomolecular markers in COVID disease prognosis, is still not well supported by available data. In order for these markers to have practical application in clinical decision-making regarding patient treatment and prognosis, it is necessary to know an optimal cut-off point, taking into consideration ethnic differences and geographic conditions. AIM: To determine the value of clinical and biomolecular markers, to predict mortality of patients with severe COVID-19 living at high altitude. METHODS: In this study, receiver operating characteristic (ROC) curves, area under the curve (AUC) of ROC, sensitivity, specificity and likelihood ratios were calculated to determine levels of clinical and biomolecular markers that best differentiate survivors versus non-survivors in severe COVID subjects that live at a high altitude setting. RESULTS: Selected cut-off values for ferritin (≥ 1225 ng/dl, p = 0.026), IL-6 (≥ 11 pg/ml, p = 0.005) and NLR (≥ 22, p = 0.008) at 24 h, as well as PaFiO2 (≤ 164 mmHg, p = 0.015), NLR (≥ 16, p = p = 0.013) and SOFA (≥ 6, p = 0.031) at 72 h, appear to have good discriminating power to differentiate survivors versus non-survivors. Additionally, odds ratios for ferritin (OR = 3.38); IL-6 (OR = 17.07); PaFiO2 (OR = 4.61); NLR 24 h (OR = 4.95); NLR 72 h (OR = 4.46), and SOFA (OR = 3.77) indicate increased risk of mortality when cut-off points were taken into consideration. CONCLUSIONS: We proposed a straightforward and understandable method to identify dichotomized levels of clinical and biomolecular markers that can discriminate between survivors and non-survivors patients with severe COVID-19 living at high altitudes.

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