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BACKGROUND: This study aimed to develop prognostic models for predicting the need for invasive mechanical ventilation (IMV) in intensive care unit (ICU) patients with COVID-19 and compare their performance with the Respiratory rate-OXygenation (ROX) index. METHODS: A retrospective cohort study was conducted using data collected between March 2020 and August 2021 at three hospitals in Rio de Janeiro, Brazil. ICU patients aged 18 years and older with a diagnosis of COVID-19 were screened. The exclusion criteria were patients who received IMV within the first 24 h of ICU admission, pregnancy, clinical decision for minimal end-of-life care and missing primary outcome data. Clinical and laboratory variables were collected. Multiple logistic regression analysis was performed to select predictor variables. Models were based on the lowest Akaike Information Criteria (AIC) and lowest AIC with significant p values. Assessment of predictive performance was done for discrimination and calibration. Areas under the curves (AUC)s were compared using DeLong's algorithm. Models were validated externally using an international database. RESULTS: Of 656 patients screened, 346 patients were included; 155 required IMV (44.8%), 191 did not (55.2%), and 207 patients were male (59.8%). According to the lowest AIC, arterial hypertension, diabetes mellitus, obesity, Sequential Organ Failure Assessment (SOFA) score, heart rate, respiratory rate, peripheral oxygen saturation (SpO2), temperature, respiratory effort signals, and leukocytes were identified as predictors of IMV at hospital admission. According to AIC with significant p values, SOFA score, SpO2, and respiratory effort signals were the best predictors of IMV; odds ratios (95% confidence interval): 1.46 (1.07-2.05), 0.81 (0.72-0.90), 9.13 (3.29-28.67), respectively. The ROX index at admission was lower in the IMV group than in the non-IMV group (7.3 [5.2-9.8] versus 9.6 [6.8-12.9], p < 0.001, respectively). In the external validation population, the area under the curve (AUC) of the ROX index was 0.683 (accuracy 63%), the AIC model showed an AUC of 0.703 (accuracy 69%), and the lowest AIC model with significant p values had an AUC of 0.725 (accuracy 79%). CONCLUSIONS: In the development population of ICU patients with COVID-19, SOFA score, SpO2, and respiratory effort signals predicted the need for IMV better than the ROX index. In the external validation population, although the AUCs did not differ significantly, the accuracy was higher when using SOFA score, SpO2, and respiratory effort signals compared to the ROX index. This suggests that these variables may be more useful in predicting the need for IMV in ICU patients with COVID-19. GOV IDENTIFIER: NCT05663528.

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BACKGROUND: In the context of the COVID-19 pandemic, the early and accurate identification of patients at risk of deterioration was crucial in overcrowded and resource-limited emergency departments. This study conducts an external validation for the evaluation of the performance of the National Early Warning Score 2 (NEWS2), the S/F ratio, and the ROX index at ED admission in a large cohort of COVID-19 patients from Colombia, South America, assessing the net clinical benefit with decision curve analysis. METHODS: A prospective cohort study was conducted on 6907 adult patients with confirmed COVID-19 admitted to a tertiary care ED in Colombia. The study evaluated the diagnostic performance of NEWS2, S/F ratio, and ROX index scores at ED admission using the area under the receiver operating characteristic curve (AUROC) for discrimination, calibration, and decision curve analysis for the prediction of intensive care unit admission, invasive mechanical ventilation, and in-hospital mortality. RESULTS: We included 6907 patients who presented to the ED with confirmed SARS-CoV-2 infection from March 2020 to November 2021. Mean age was 51 (35-65) years and 50.4% of patients were males. The rate of intensive care unit admission was 28%, and in-hospital death was 9.8%. All three scores have good discriminatory performance for the three outcomes based on the AUROC. S/F ratio showed miscalibration at low predicted probabilities and decision curve analysis indicated that the NEWS2 score provided a greater net benefit compared to other scores across at a 10% threshold to decide ED admission at a high-level of care facility. CONCLUSIONS: The NEWS2, S/F ratio, and ROX index at ED admission have good discriminatory performances in COVID-19 patients for the prediction of adverse outcomes, but the NEWS2 score has a higher net benefit underscoring its clinical utility in optimizing patient management and resource allocation in emergency settings.

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BACKGROUND: Breast cancer-related inflammation is critical in tumorigenesis, cancer progression, and patient prognosis. Several inflammatory markers derived from peripheral blood cells count, such as the neutrophil-lymphocyte ratio (NLR), platelet-lymphocyte ratio (PLR), monocyte-lymphocyte ratio (MLR), and systemic immune-inflammation index (SII) are considered as prognostic markers in several types of malignancy. METHODS: We investigate and validate a prognostic model in early patients with breast cancer to predict disease-free survival (DFS) based on readily available baseline clinicopathological prognostic factors and preoperative peripheral blood-derived indexes. RESULTS: We analyzed a training cohort of 710 patients and 2 external validation cohorts of 980 and 157 patients with breast cancer, respectively, with different demographic origins. An elevated preoperative NLR is a better DFS predictor than others scores. The prognostic model generated in this study was able to classify patients into 3 groups with different risks of relapse based on ECOG-PS, presence of comorbidities, T and N stage, PgR status, and NLR. CONCLUSION: Prognostic models derived from the combination of clinicopathological features and peripheral blood indices, such as NLR, represent attractive markers mainly because they are easily detectable and applicable in daily clinical practice. More comprehensive prospective studies are needed to unveil their actual effectiveness.

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OBJECTIVE: To compare the predictive performance of the current clinical prediction models for predicting intravesical recurrence (IVR) after radical nephroureterectomy (RNU) in patients with upper tract urothelial carcinoma (UTUC). METHODS: We retrospectively analysed upper tract urothelial carcinoma patients who underwent radical nephroureterectomy in our centre from January 2009 to December 2019. We used the propensity score matching (PSM) method to adjust the confounders between the IVR and non-IVR groups. Additionally, Xylinas' reduce model and full model, Zhang's model, and Ishioka's risk stratification model were used to retrospectively calculate predictions for each patient. Receiver operating characteristic (ROC) curves were generated, and the areas under the curves (AUCs) were compared to identify the method with the highest predictive value. RESULTS: We included 217 patients with a median follow-up of 41 months, of which 57 had IVR. After PSM analysis, 52 pairs of well-matched patients were included in the comparative study. No significant difference was found in clinical indicators besides hydronephrosis. The model comparison showed that the AUCs of the reduced Xylinas' model for 12 months, 24 months, and 36 months were 0.69, 0.73, and 0.74, respectively, and those of the full Xylinas' model were 0.72, 0.75, and 0.74, respectively. The AUC of Zhang's model for 12 months, 24 months, and 36 months was 0.63, 0.71, and 0.71, respectively, the performance of Ishioka's model is that the AUC of 12 months, 24 months and 36 months was 0.66, 0.71, and 0.74, respectively. CONCLUSION: The external verification results of the four models show that more comprehensive data and a larger sample size of patients are needed to strengthen the models' derivation and updating procedure, to better apply them to different populations.

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Even with over 80% of the population being vaccinated against COVID-19, the disease continues to claim victims. Therefore, it is crucial to have a secure Computer-Aided Diagnostic system that can assist in identifying COVID-19 and determining the necessary level of care. This is especially important in the Intensive Care Unit to monitor disease progression or regression in the fight against this epidemic. To accomplish this, we merged public datasets from the literature to train lung and lesion segmentation models with five different distributions. We then trained eight CNN models for COVID-19 and Common-Acquired Pneumonia classification. If the examination was classified as COVID-19, we quantified the lesions and assessed the severity of the full CT scan. To validate the system, we used Resnetxt101 Unet++ and Mobilenet Unet for lung and lesion segmentation, respectively, achieving accuracy of 98.05%, F1-score of 98.70%, precision of 98.7%, recall of 98.7%, and specificity of 96.05%. This was accomplished in just 19.70 s per full CT scan, with external validation on the SPGC dataset. Finally, when classifying these detected lesions, we used Densenet201 and achieved accuracy of 90.47%, F1-score of 93.85%, precision of 88.42%, recall of 100.0%, and specificity of 65.07%. The results demonstrate that our pipeline can correctly detect and segment lesions due to COVID-19 and Common-Acquired Pneumonia in CT scans. It can differentiate these two classes from normal exams, indicating that our system is efficient and effective in identifying the disease and assessing the severity of the condition.

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Introduction Over-investigation of head computed tomography (CT) has been observed in children with TBI. Long-term effects from a head CT brain scan have been addressed and those should be balanced. A nomogram is a simple prediction tool that has been reported for predicting intracranial injuries following a head CT of the brain in TBI children in literature. This study aims to validate the performance of the nomogram using unseen data. Additionally, the secondary objective aims to estimate the net benefit of the nomogram by decision curve analysis (DCA). Methods We conducted a retrospective cohort study with 64 children who suffered from traumatic brain injury (TBI) and underwent a CT of the brain. Nomogram's scores were assigned according to various variables in each patient; therefore sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), accuracy and F1 score were estimated by the cross-tabulation of the actual results and the predicted results. Additionally, the benefits of a nomogram were compared with "None" and "All" protocols using DCA. Results There were 64 children with TBI who underwent a head CT in the present study. From the cross-tabulation, the nomogram had a sensitivity of 0.60 (95%CI 0.29­ 0.90), specificity of 0.96 (0.91­1.0), PPV of 0.75 (0.44­1.0), NPV of 0.92 (0.86­0.99), accuracy of 0.90 (0.83­0.97), and an F1 score of 0.66 (0.59­0.73). Also, the area under the curve was 0.78 which was defined as acceptable performance. For the DCA at 0.1 high-risk threshold, the net benefit of the nomogram was 0.75, whereas the "All" protocol had the net benefit of 0.40 which was obviously different. Conclusion A nomogram is a suitable method as an alternative prediction tool in general practice that has advantages over other protocols.

Introdução A investigação excessiva da tomografia computadorizada (TC) de crânio tem sido observada em crianças com TCE. Os efeitos a longo prazo de uma tomografia computadorizada de crânio foram abordados e devem ser equilibrados. Um nomograma é uma ferramenta de predição simples que foi relatada na literatura para prever lesões intracranianas após uma tomografia computadorizada de crânio em crianças com TCE. Este estudo tem como objetivo validar o desempenho do nomograma usando dados não vistos. Adicionalmente, o objetivo secundário visa estimar o benefício líquido do nomograma por meio da análise da curva de decisão (DCA). Métodos Realizamos um estudo de coorte retrospectivo com 64 crianças que sofreram traumatismo cranioencefálico (TCE) e foram submetidas a tomografia computadorizada de crânio. As pontuações do Nomograma foram atribuídas de acordo com diversas variáveis em cada paciente; portanto, sensibilidade, especificidade, valor preditivo positivo (VPP), valor preditivo negativo (VPN), acurácia e escore F1 foram estimados pela tabulação cruzada dos resultados reais e dos resultados previstos. Além disso, os benefícios de um nomograma foram comparados com os protocolos "Nenhum" e "Todos" usando DCA. Resultados Houve 64 crianças com TCE que foram submetidas a tomografia computadorizada de crânio no presente estudo. A partir da tabulação cruzada, o nomograma apresentou sensibilidade de 0,60 (IC95% 0,29­0,90), especificidade de 0,96 (0,91­ 1,0), VPP de 0,75 (0,44­1,0), VPN de 0,92 (0,86­0,99), acurácia de 0,90 (0,83­0,97) e uma pontuação F1 de 0,66 (0,59­0,73). Além disso, a área sob a curva foi de 0,78, definida como desempenho aceitável. Para o DCA no limiar de alto risco de 0,1, o benefício líquido do nomograma foi de 0,75, enquanto o protocolo "Todos" teve o benefício líquido de 0,40, o que foi obviamente diferente. Conclusão Um nomograma é um método adequado como ferramenta alternativa de predição na prática geral que apresenta vantagens sobre outros protocolos.

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There are several equations based on bioelectrical impedance analysis (BIA) to estimate with high precision appendicular skeletal muscle mass (ASM). However, most of the external validation studies have reported that these equations are inaccurate or biased when applied to different populations. Furthermore, none of the published studies has derived correction factors (CFs) in samples of community-dwelling older adults, and none of the published studies have assessed the influence of the dual-energy X-ray absorptiometry (DXA) model on the validation process. This study assessed the agreement between six BIA equations and DXA to estimate ASM in non-Caucasian older adults considering the DXA model and proposed a CF for three of them. This analysis included 547 non-institutionalized subjects over 60 years old from the northwest of Mexico who were physically independent and without cognitive impairment: 192 subjects were measured using DXA Hologic, while 355 were measured by DXA Lunar. The agreement between each of the equations and DXA was tested considering the DXA model used as a reference method for the design of each equation, using the Bland and Altman procedure, a paired t test, and simple linear regression as objective tests. This process was supported by the differences reported in the literature and confirmed in a subsample of 70 subjects measured with both models. Only six published BIA equations were included. The results showed that four equations overestimated ASMDXA, and two underestimated it (p < 0.001, 95% CI for Kim's equation:-5.86--5.45, Toselli's:-0.51--0.15, Kyle's: 1.43-1.84, Rangel-Peniche's: 0.32-0.74, Sergi's: 0.83-1.23, and Yoshida's: 4.16-4.63 kg). However, Toselli's, Kyle's and Rangel-Peniche's equations were the only ones that complied with having a homogeneous bias. This finding allowed the derivation of CFs, which consisted of subtracting or adding the mean of the differences from the original equation. After estimating ASM applying the respective CF, the new ASM estimations showed no significant bias and its distribution remained homogeneously distributed. Therefore, agreement with DXA in the sample of non-Caucasian was achieved. Adding valid CFs to some BIA equations allowed to reduce the bias of some equations, making them valid to estimate the mean values of ASM at group level.

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Resumen Introducción: la identificación de los pacientes con mayor riesgo de progresar a falla renal es fundamental para la planeación del tratamiento en la enfermedad renal crónica, pero no ha podido llevarse a cabo consistentemente. Los modelos de predicción podrían ser una herramienta útil, sin embargo, su usabilidad en la Enfermedad Renal Crónica es limitada hasta ahora y no se comprenden muy bien las barreras y limitaciones. Métodos: se desarrolló una revisión de alcance de la literatura disponible sobre modelos predictivos de falla renal o reglas de pronóstico en pacientes con Enfermedad Renal Crónica. Las búsquedas se realizaron sistemáticamente en Cochrane, Pubmed y Embase. Se realizó una revisión ciega e independiente por dos evaluadores para identificar estudios que informaran sobre el desarrollo, la validación o la evaluación del impacto de un modelo construido para predecir la progresión al estadio avanzado de la enfermedad renal crónica. Se realizó una evaluación crítica de la calidad de la evidencia proporcionada con el sistema GRADE (Grading of Recommendations Assessment, Development and Evaluation). Resultados: de 1279 artículos encontrados, fueron incluidos 19 estudios para la síntesis cualitativa final. La mayoría de los estudios eran primarios, con diseños observacionales retrospectivos y unos pocos correspondieron a revisiones sistemáticas. No se encontraron guías de práctica clínica. La síntesis cualitativa evidenció gran heterogeneidad en el desarrollo de los modelos, así como en el reporte de las medidas de desempeño global, la validez interna y la falta de validez externa en la mayoría de los estudios. La calificación de la evidencia arrojó una calidad global baja, con inconsistencia entre los estudios e importantes limitaciones metodológicas. Conclusiones: la mayoría de los modelos predictivos disponibles, no han sido adecuadamente validados y, por tanto, se consideran de uso limitado para evaluar el pronóstico individual del paciente con enfermedad renal crónica. Por lo tanto, se requieren esfuerzos adicionales para centrar el desarrollo e implementación de modelos predictivos en la validez externa y la usabilidad y disminuir la brecha entre la generación, la síntesis de evidencia y la toma de decisiones en el ámbito del cuidado del paciente.

Abstract Background: the identification of patients at higher risk of progressing to kidney failure is essential for treatment planning in chronic kidney disease, but it has not been possible to do this consistently. Predictive models could be a useful tool, however, their usability in chronic kidney disease is limited and the barriers and limitations are not well understood. Methods: a scoping review of the available literature on ESRD predictive models or prognostic rules in chronic kidney disease patients was developed. Searches were systematically executed on Cochrane, MEDLINE, and Embase. a blind and independent review was carried out by two evaluators to identify studies that reported on the development, validation, or impact assessment of a model constructed to predict the progression to an advanced stage of chronic kidney disease. A critical evaluation of the quality of the evidence provided with the GRADE system (Grading of Recommendations Assessment, Development and Evaluation) was made. Findings: of 1279 articles found, 19 studies were included for the final qualitative synthesis. Most of the studies were primary, with retrospective observational designs and a few corresponded to systematic reviews. No clinical practice guidelines were found. The qualitative synthesis showed high heterogeneity in the development of the models, as well as in the reporting of global performance measures, internal validity, and the lack of external validity in most of the studies. The evidence rating was of low overall quality, with inconsistency between studies and important methodological limitations. Conclusions: most of the available predictive models have not been adequately validated and, therefore, are of limited use to assess the individual prognosis of patients with chronic kidney disease. Therefore, additional efforts are required to focus the development and implementation of predictive models on external validity and usability and bridge the gap between generation, synthesis of evidence, and decision-making in the field of patient care.

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BACKGROUND: Neck pain is one of the leading causes of disability in most countries and it is likely to increase further. Numerous prognostic models for people with neck pain have been developed, few have been validated. In a recent systematic review, external validation of three promising models was advised before they can be used in clinical practice. OBJECTIVE: The purpose of this study was to externally validate three promising models that predict neck pain recovery in primary care. METHODS: This validation cohort consisted of 1311 patients with neck pain of any duration who were prospectively recruited and treated by 345 manual therapists in the Netherlands. Outcome measures were disability (Neck Disability Index) and recovery (Global Perceived Effect Scale) post-treatment and at 1-year follow-up. The assessed models were an Australian Whiplash-Associated Disorders (WAD) model (Amodel), a multicenter WAD model (Mmodel), and a Dutch non-specific neck pain model (Dmodel). Models' discrimination and calibration were evaluated. RESULTS: The Dmodel and Amodel discriminative performance (AUC < 0.70) and calibration measures (slope largely different from 1) were poor. The Mmodel could not be evaluated since several variables nor their proxies were available. CONCLUSIONS: External validation of promising prognostic models for neck pain recovery was not successful and their clinical use cannot be recommended. We advise clinicians to underpin their current clinical reasoning process with evidence-based individual prognostic factors for recovery. Further research on finding new prognostic factors and developing and validating models with up-to-date methodology is needed for recovery in patients with neck pain in primary care.

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The burden of adolescent depression is high in low- and middle-income countries (LMICs), yet research into prevention is lacking. Development and validation of models to predict individualized risk of depression among adolescents in LMICs is rare but crucial to ensure appropriate targeting of preventive interventions. We assessed the ability of a model developed in Brazil, a middle-income country, to predict depression in an existing culturally different adolescent cohort from Nepal, a low-income country with a large youth population with high rates of depression. Data were utilized from the longitudinal study of 258 former child soldiers matched with 258 war-affected civilian adolescents in Nepal. Prediction modelling techniques were employed to predict individualized risk of depression at age 18 or older in the Nepali cohort using a penalized logistic regression model. Following a priori exclusions for prior depression and age, 55 child soldiers and 71 war-affected civilians were included in the final analysis. The model was well calibrated, had good overall performance, and achieved good discrimination between depressed and non-depressed individuals with an area under the curve (AUC) of 0.73 (bootstrap-corrected 95% confidence interval 0.62-0.83). The Brazilian model comprising seven matching sociodemographic predictors, was able to stratify individualized risk of depression in a Nepali adolescent cohort. Further testing of the model's performance in larger socio-culturally diverse samples in other geographical regions should be attempted to test the model's wider generalizability.

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Despite recommendations for the routine HIV testing of all sexually active individuals, a significant percentage of HIV-positive adults are unaware of their HIV status. Therefore, a number of strategies have been implemented to expand HIV testing, which in turn makes it necessary to develop tools for identifying patients with unknown HIV status. This study presents the results of an external validation of an electronic phenotyping algorithm for identifying HIV status and its application on a retrospective cohort in order to explore temporal trends of HIV knowledge status and associated factors.

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Resumen: Objetivo: El European System for Cardiac Operative Risk Evaluation (EuroSCORE) estratifica el riesgo quirúrgico en cirugía cardiaca de manera fácil y accesible; se validó en Norteamérica con buenos resultados, pero en muchos países de Latinoamérica se utiliza rutinariamente sin validación previa. Nuestro objetivo fue validar EuroSCORE en pacientes con cirugía valvular en el Instituto Nacional de Cardiología Ignacio Chávez (INCICh) de México. Métodos: Se aplicaron los modelos de EuroSCORE aditivo y logístico para predecir mortalidad en pacientes con cirugía valvular de marzo de 2004 a marzo de 2008. Se usó la prueba de bondad de ajuste de Hosmer-Lemeshow para evaluar la calibración. Se calculó el área bajo la curva ROC para determinar la discriminación. Resultados: Se incluyeron 1,188 pacientes con edades de 51.3 ± 14.5 años, 52% mujeres. Hubo diferencias significativas en la prevalencia de los factores de riesgo entre la población del INCICh y del EuroSCORE. La mortalidad total fue de 9.68% con predichas de 5% y 5.6% por EuroSCORE aditivo y logístico. De acuerdo a EuroSCORE aditivo tenían riesgo bajo 11.3%, intermedio 52.9% y alto 35.9%; para estos grupos la mortalidad fue de 0.7%, 6.4% y 17.4% contra las predichas de 2%, 3.9% y 7.64%. La prueba de Hosmer-Lemeshow tuvo una p < 0.001 para ambos modelos, y el área bajo la curva ROC de 0.707 y de 0.694 para EuroSCORE aditivo y logístico. Conclusión: En el INCICh el 88.7% de los pacientes con cirugía valvular tuvieron riesgo intermedio a alto y EuroSCORE subestimó el riesgo de mortalidad.

Abstract: Objective: The EuroSCORE (European System for cardiac operative risk evaluation) stratifies cardiac risk surgery in easy and accessible manner; it was validated in North America with good results but in many countries of Latin America is used routinely without prior validation. Our objective was to validate the EuroSCORE in patients with cardiac valve surgery at the Instituto Nacional de Cardiología Ignacio Chávez (INCICh) in México. Methods: EuroSCORE additive and logistic models were used to predict mortality in adults undergoing cardiac valve surgery from march 2004 to march 2008. The goodness of fit test of Hosmer-Lemeshow was used to evaluate the calibration. The area under the ROC curve was calculated to determinate discrimination. Results: We included 1188 patients with ages of 51.3 ± 14.5 years, 52% women. There were significant differences in the prevalence of risk factors among the INCICh and the EuroSCORE populations. Total mortality was 9.68% versus 5% and 5.6% predicted by additive and logistic EuroSCORE. According to additive EuroSCORE the risk was low in 11.3%, intermediate in 52.9% and high in 35.9%; for these groups the mortality was .7%, 6.34% and 17.4% against those predicted of 2%, 3.9% and 7.64%. Hosmer-Lemeshow test had a P < .001 for both models and the area under the ROC curve was .707 and .694 for additive and logistic EuroSCORE. Conclusion: In the INCICh 88.7% of patients with cardiac valve surgery had intermediate to high risk and EuroSCORE underestimated the risk of mortality.

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OBJECTIVE: The EuroSCORE (European System for cardiac operative risk evaluation) stratifies cardiac risk surgery in easy and accessible manner; it was validated in North America with good results but in many countries of Latin America is used routinely without prior validation. Our objective was to validate the EuroSCORE in patients with cardiac valve surgery at the Instituto Nacional de Cardiología Ignacio Chávez (INCICh) in México. METHODS: EuroSCORE additive and logistic models were used to predict mortality in adults undergoing cardiac valve surgery from march 2004 to march 2008. The goodness of fit test of Hosmer-Lemeshow was used to evaluate the calibration. The area under the ROC curve was calculated to determinate discrimination. RESULTS: We included 1188 patients with ages of 51.3±14.5 years, 52% women. There were significant differences in the prevalence of risk factors among the INCICh and the EuroSCORE populations. Total mortality was 9.68% versus 5% and 5.6% predicted by additive and logistic EuroSCORE. According to additive EuroSCORE the risk was low in 11.3%, intermediate in 52.9% and high in 35.9%; for these groups the mortality was .7%, 6.34% and 17.4% against those predicted of 2%, 3.9% and 7.64%. Hosmer-Lemeshow test had a P<.001 for both models and the area under the ROC curve was .707 and .694 for additive and logistic EuroSCORE. CONCLUSION: In the INCICh 88.7% of patients with cardiac valve surgery had intermediate to high risk and EuroSCORE underestimated the risk of mortality.

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