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BACKGROUND: The quick Sequential Sepsis-related Organ Failure Assessment (qSOFA) is a score that has been proposed to quickly identify patients at higher risk of death. OBJECTIVE: To describe the usefulness of the qSOFA score to predict in-hospital mortality in cancer patients. MATERIAL AND METHODS: Cross-sectional study carried out between January 2021 and December 2022. Hospital mortality was the dependent variable. The area under the ROC curve (AUC) was calculated to determine the discriminative ability of qSOFA to predict in-hospital mortality. RESULTS: A total of 587 cancer patients were included. A qSOFA score higher than 1 obtained a sensitivity of 57.2%, specificity of 78.5%, a positive predictive value of 55.4% and negative predictive value of 79.7%. The AUC of qSOFA for predicting in-hospital mortality was 0.70. In-hospital mortality of patients with qSOFA scores of 2 and 3 points was 52.7 and 64.4%, respectively. In-hospital mortality was 31.9% (187/587). CONCLUSION: qSOFA showed acceptable discriminative ability for predicting in-hospital mortality in cancer patients.

ANTECEDENTES: El quick Sequential Sepsis-related Organ Failure Assessment (qSOFA) es una puntuación propuesta para identificar de forma rápida a pacientes con mayor probabilidad de morir. OBJETIVO: Describir la utilidad de la puntuación qSOFA para predecir mortalidad hospitalaria en pacientes con cáncer. MATERIAL Y MÉTODOS: Estudio transversal realizado entre enero de 2021 y diciembre de 2022. La mortalidad hospitalaria fue la variable dependiente. Se calculó el área bajo la curva ROC (ABC) para determinar la capacidad discriminativa de qSOFA para predecir mortalidad hospitalaria. RESULTADOS: Se incluyeron 587 pacientes con cáncer. La puntuación qSOFA < 1 obtuvo una sensibilidad de 57.2 %, una especificidad de 78.5 %, un valor predictivo positivo de 55.4 % y un valor predictivo negativo de 79.7 %. El ABC de qSOFA para predecir mortalidad hospitalaria fue de 0.70. La mortalidad hospitalaria de los pacientes con qSOFA de 2 y 3 puntos fue de 52.7 y 64.4 %, respectivamente. La mortalidad hospitalaria fue de 31.9 % (187/587). CONCLUSIÓN: qSOFA mostró capacidad discriminativa aceptable para predecir mortalidad hospitalaria en pacientes con cáncer.

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Resumen Antecedentes: El quick Sequential Sepsis-related Organ Failure Assessment (qSOFA) es una puntuación propuesta para identificar de forma rápida a pacientes con mayor probabilidad de morir. Objetivo: Describir la utilidad de la puntuación qSOFA para predecir mortalidad hospitalaria en pacientes con cáncer. Material y métodos: Estudio transversal realizado entre enero de 2021 y diciembre de 2022. La mortalidad hospitalaria fue la variable dependiente. Se calculó el área bajo la curva ROC (ABC) para determinar la capacidad discriminativa de qSOFA para predecir mortalidad hospitalaria. Resultados: Se incluyeron 587 pacientes con cáncer. La puntuación qSOFA < 1 obtuvo una sensibilidad de 57.2 %, una especificidad de 78.5 %, un valor predictivo positivo de 55.4 % y un valor predictivo negativo de 79.7 %. El ABC de qSOFA para predecir mortalidad hospitalaria fue de 0.70. La mortalidad hospitalaria de los pacientes con qSOFA de 2 y 3 puntos fue de 52.7 y 64.4 %, respectivamente. La mortalidad hospitalaria fue de 31.9 % (187/587). Conclusión: qSOFA mostró capacidad discriminativa aceptable para predecir mortalidad hospitalaria en pacientes con cáncer.

Abstract Background: The quick Sequential Sepsis-related Organ Failure Assessment (qSOFA) is a score that has been proposed to quickly identify patients at higher risk of death. Objective: To describe the usefulness of the qSOFA score to predict in-hospital mortality in cancer patients. Material and methods: Cross-sectional study carried out between January 2021 and December 2022. Hospital mortality was the dependent variable. The area under the ROC curve (AUC) was calculated to determine the discriminative ability of qSOFA to predict in-hospital mortality. Results: A total of 587 cancer patients were included. A qSOFA score higher than 1 obtained a sensitivity of 57.2 %, specificity of 78.5 %, a positive predictive value of 55.4 % and negative predictive value of 79.7 %. The AUC of qSOFA for predicting in-hospital mortality was 0.70. In-hospital mortality of patients with qSOFA scores of 2 and 3 points was 52.7 and 64.4 %, respectively. In-hospital mortality was 31.9 % (187/587). Conclusions: qSOFA showed acceptable discriminative ability for predicting in-hospital mortality in cancer patients.

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BACKGROUND: Patients with hypotension usually receive intravenous fluids, but only 50% will respond to fluid administration. We aimed to assess the intra and interobserver agreement to evaluate fluid tolerance through diverse ultrasonographic methods. METHODS: We prospectively included critically ill patients on mechanical ventilation. One trained intensivist and two intensive care residents obtained the left ventricular outflow tract velocity-time integral (VTI) variability, inferior vena cava (IVC) distensibility index, internal jugular vein (IJV) distensibility index, and each component of the venous excess ultrasound (VExUS) system. We obtained the intraclass correlation coefficient (ICC) and Gwet's first-order agreement coefficient (AC1), as appropriate. RESULTS: We included 32 patients. In-training observers were unable to assess the VTI-variability in two patients. The interobserver agreement was moderate to evaluate the IJV-distensibility index (AC1 0.54, CI 95% 0.29-0.80), fair to evaluate VTI-variability (AC1 0.39, CI 95% 0.12-0.66), and absent to evaluate the IVC-distensibility index (AC1 0.19, CI 95% - 0.07 to 0.44). To classify patients according to their VExUS grade, the intraobserver agreement was good, and the interobserver agreement was moderate (AC1 0.52, CI 95% 0.34-0.69). CONCLUSIONS: Point-of-care ultrasound is frequently used to support decision-making in fluid management. However, we observed that the VTI variability and IVC-distensibility index might require further training of the ultrasound operators to be clinically useful. Our findings suggest that the IJV-distensibility index and the VExUS system have acceptable reproducibility among in-training observers.

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PURPOSE: We evaluated the renal arterial resistive index (RRI), urine monocyte chemotactic protein 1 (uMCP-1), and urine neutrophil gelatinase-associated lipocalin (uNGAL) to predict acute kidney injury (AKI) in critically ill cancer patients. METHODS: In this prospective study, we included patients without AKI. We compared the area under the curve (AUC) of RRI, uMCP-1, and uNGAL to predict any stage of AKI and stage-3 AKI with the DeLong method, and we established cutoff points with the Youden index. RESULTS: We included 64 patients, and 43 (67.2%) developed AKI. The AUC to predict AKI were: 0.714 (95% CI 0.587-0.820) for the RRI, 0.656 (95% CI 0.526-0.770) for uMCP-1, and 0.677 (95% CI 0.549-0.789) for uNGAL. The AUC to predict stage-3 AKI were: 0.740 (95% CI 0.615-0.842) for the RRI, 0.757 (95% CI 0.633-0.855) for uMCP-1, and 0.817 (95% CI 0.701-0.903) for uNGAL, without statistical differences among them. For stage 3 AKI prediction, the sensitivity and specificity were: 56.3% and 87.5% for a RRI > 0.705; 70% and 79.2% for an uMCP-1 > 2169 ng/mL; and 87.5% and 70.8% for a uNGAL > 200 ng/mL. The RRI was significantly correlated to age (r = 0.280), estimated glomerular filtration rate (r = - 0.259), mean arterial pressure (r = - 0.357), and serum lactate (r = 0.276). CONCLUSION: The RRI, uMCP-1, and uNGAL have a similar ability to predict AKI. The RRI is more specific, while urine biomarkers are more sensitive to predict stage 3 AKI. The RRI correlates with hemodynamic variables. The novel uMCP-1 could be a useful biomarker that needs to be extensively studied.

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AIM: We aimed to identify risk factors associated with acute kidney injury (AKI) and to analyse 1-year mortality after oncological surgery. METHODS: We retrospectively included 434 adult patients admitted to the intensive care unit (ICU) after oncological surgery, and classified AKI according to the Kidney Disease: Improving Global Outcomes criteria. We performed logistic regression and Cox regression analyses to evaluate AKI and mortality risk factors. RESULTS: Sixty-one percent of patients (n = 264) developed AKI. Previous abdominal radiotherapy and abdominal surgical packing were independently associated with stage 2 and 3 AKI, with adjusted odds ratio (OR) of 2.6 (95% confidence interval [CI] 1.3-5.5, p = .010) and OR of 2.6 (95% CI 1.2-5.5, p = .014), respectively. Other independent risk factors were: glomerular filtration rate (eGFR) <60 ml/min/1.73m2 (OR 3.6, 95% CI 1.2-11.4, p = .028), abdominal surgery 2.6 (1.4-4.9, p = .003), intraoperative diuresis <1 ml/k/h (OR 2.4, 95% CI 1.4-4.0, p = .001), sepsis (OR 2.5, 95% CI 1.3-4.6, p = .002) and mechanical ventilation at ICU admission (OR 7.7, 95% CI 3.2-18.6, p < .001). Stage 2 and stage 3 AKI were independently associated with 1-year mortality, with adjusted hazard ratios (HR) of 2.6 (95% CI 1.3-5.0, p = .005) and HR of 5.0 (95% CI 2.6-9.6, p < .001), respectively. Additionally, patients who had postsurgical AKI, had a lower eGFR at 1-year follow-up. These findings may be limited by the retrospective single centre design of our study. CONCLUSION: In addition to the conventional risk factors, our results suggest that abdominal radiotherapy and abdominal surgical packing could be independent risk factors for AKI after oncological surgery.

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OBJECTIVE: To determine the outcomes of hospitalized cancer patients requiring intensive care unit (ICU) intervention and receiving palliative care. MATERIALS AND METHODS: An observational retrospective study was completed at a single academic critical care unit in Mexico City. All hospitalized cancer patients who were evaluated by the intensive care team to assess need for ICU were included between January and December 2018. RESULTS: During the study period, the ICU group made 408 assessments of critically ill cancer patients in noncritical hospitalized areas. In total, 24.2% (99/408) of the patients in this population were consulted by the palliative care team. Of the patients evaluated, 46.5% (190/408) had advanced stage, but only 28.4% were receiving care by the palliative care team. The only risk factor for hospital mortality in the multivariate analysis was the quick Sequential Organ Failure Assessment (qSOFA) score at the time of the consultation by the ICU group (HR = 2.10, 95% CI = 1.34-3.29, p = 0.001). The median time between palliative care consultation and death was 3 days (IQR = 2-22). A total of 63% (37/58) of patients who were discharged from the hospital died during follow-up. The median follow-up time was 55 days (95% CI = 26.9-83.0). The overall mortality rate for the entire group during hospitalization and after hospital discharge was 80.8% (80/99). CONCLUSION: Fewer than 3 out of 10 hospitalized cancer patients requiring admission to the ICU were evaluated by the palliative care team despite having incurable cancer. The qSOFA score of patients at the time of the ICU consultation was the only risk factor for mortality during hospitalization. Future research efforts in Mexico should focus on earlier integration of palliation care with usual oncology care in incurable cancer patients.

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Abstract: Objective: To develop a score to predict the need for intensive care unit (ICU) admission in Covid-19. Materials and methods: We assessed patients admitted to a Covid-19 center in Mexico. Patients were segregated into a group that required ICU admission, and a group that never required ICU admission. By logistic regression, we derived predictive models including clinical, laboratory, and imaging findings. The ABC-GOALS was constructed and compared to other scores. Results: We included 329 and 240 patients in the development and validation cohorts, respectively. One-hundred-fifteen patients from each cohort required ICU admission. The clinical (ABC-GOALSc), clinical+laboratory (ABC-GOALScl), clinical+laboratory+image (ABC-GOALSclx) models area under the curve were 0.79 (95%CI=0.74-0.83) and 0.77 (95%CI=0.71-0.83), 0.86 (95%CI=0.82-0.90) and 0.87 (95%CI=0.83-0.92), 0.88 (95%CI=0.84-0.92) and 0.86 (95%CI=0.81-0.90), in the development and validation cohorts, respectively. The ABC-GOALScland ABC-GOALSclxoutperformed other Covid-19 and pneumonia predictive scores. Conclusion: ABC-GOALS is a tool to timely predict the need for admission to ICU in Covid-19.

Resumen: Objetivo: Desarrollar un puntaje predictivo de la necesidad de ingreso a una unidad de cuidados intensivos (UCI) en Covid-19. Material y métodos: Se evaluaron pacientes ingresados por Covid-19 en México. Se dividieron en un grupo que requirió ingreso a UCI y un grupo que nunca lo requirió. Se derivaron modelos predictivos incluyendo variables clínicas, de laboratorio e imagen y se integraron en el puntaje ABC-GOALS. Resultados: Se incluyeron 329 y 240 pacientes en cohortes de desarrollo y validación, respectivamente. Ciento quince pacientes de cada cohorte requirieron ingreso a UCI. Las áreas bajo la curva de los modelos clínico (ABC-GOALSc), clínico+laboratorio (ABC-GOALScl), clínico+laboratorio+imagen (ABC-GOALSclx) fueron 0.79 (IC95%=0.74-0.83) y 0.77 (IC95%=0.71-0.83); 0.86 (IC95%=0.82-0.90) y 0.87 (IC95%=0.83-0.92); 0.88 (IC95%=0.84-0.92) y 0.86 (IC95%=0.81-0.90) en las cohortes de derivación y validación, respectivamente. El desempeño del ABC-GOALS fue superior a otros puntajes de riesgo. Conclusión: ABC-GOALS es una herramienta para predecir oportunamente la necesidad de ingreso a UCI en Covid-19.

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OBJECTIVE: To develop a score to predict the need for ICU admission in COVID-19. METHODS: We assessed patients admitted to a COVID-19 center in Mexico. Patients were segregated into a group that required ICU admission, and a group that never required ICU admission. By logistic regression, we derived predictive models including clinical, laboratory, and imaging findings. The ABC-GOALS was constructed and compared to other scores. RESULTS: We included 329 and 240 patients in the development and validation cohorts, respectively. One-hundred-fifteen patients from each cohort required ICU admission. The clinical (ABC-GOALSc), clinical+laboratory (ABC-GOALScl), clinical+laboratory+image (ABC-GOALSclx) models area under the curve were 0.79 (95%CI=0.74-0.83) and 0.77 (95%CI=0.71-0.83), 0.86 (95%CI=0.82-0.90) and 0.87 (95%CI=0.83-0.92), 0.88 (95%CI=0.84-0.92) and 0.86 (95%CI=0.81-0.90), in the development and validation cohorts, respectively. The ABC-GOALScl and ABC-GOALSclx outperformed other COVID-19 and pneumonia predictive scores. CONCLUSION: ABC-GOALS is a tool to timely predict the need for admission to ICU in COVID-19.

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BACKGROUND: The quick sequential organ failure assessment (qSOFA) and the Eastern Cooperative Oncologic Group (ECOG) scale are simple and easy parameters to measure because they do not require laboratory tests. The objective of this study was to compare the discriminatory capacity of the qSOFA and ECOG to predict hospital mortality in postsurgical cancer patients without infection. METHODS: During the period 2013-2017, we prospectively collected data of all patients without infection who were admitted to the ICU during the postoperative period, except those who stayed in the ICU for <24 hours or patients under 18 years. The ECOG score during the last month before hospitalization and the qSOFA performed during the first hour after admission to the intensive care unit (ICU) were collected. The primary outcome for this study was the in-hospital mortality rate. RESULTS: A total of 315 patients were included. The ICU and hospital mortality rates were 6% and 9.2%, respectively. No difference was observed between the qSOFA [AUC=0.75 (95% CI = 0.69-0.79)] and the ECOG scores [AUC=0.68 (95%CI =0.62-0.73)] (p=0.221) for predicting in-hospital mortality. qSOFA greater than 1 predicted in-hospital mortality with a high sensitivity (100%) but low specificity (38.8%); positive predictive value of 26.3% and negative predictive value of 93.1% compared to 74.4% of specificity, 55.1% of sensitivity%; positive predictive value of 18% and negative predictive value of 94.2% for an ECOG score greater than 1. Multivariable Cox regression analysis identified two independent predicting factors of in-hospital mortality, which included ECOG score during the last month before hospitalization (HR: 1.46; 95 % CI: 1.06-2.00); qSOFA calculated in the first hours after ICU admission (OR: 3.17; 95 % CI: 1.79-5.63). CONCLUSION: No difference was observed between the qSOFA and ECOG for predicting in-hospital mortality. The qSOFA score performed during the first hour after admission to the ICU and ECOG scale during the last month before hospitalization were associated with in-hospital mortality in postsurgical cancer patients without infection. The qSOFA and ECOG score have a potential to be included as early warning tools for hospitalized postsurgical cancer patients without infection.

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RATIONALE: Acute kidney injury (AKI) is a frequent complication in critically ill cancer patients. OBJECTIVES: To assess plasma neutrophil gelatinase-associated lipocalin (NGAL) levels and risks factors associated with AKI and mortality. METHODS: We recruited 96 critically ill cancer patients and followed them prospectively. Plasma NGAL levels were determined at intensive care unit (ICU) admission and at 48 hours. We generated receiver operating characteristic curves to assess the ability of NGAL to predict AKI. Logistic regression analysis was performed to determine risks factors associated with AKI. Cox-regression analysis was performed to evaluate 6-month mortality. MEASUREMENTS AND MAIN RESULTS: From 96 patients, 60 (63%) developed AKI and 33 (55%) were classified as stages 2 and 3. In patients without AKI at admission, plasma NGAL levels revealed an area under the curve (AUC) = 0.522 for all AKI stages and an AUC = 0.573 for stages 2 and 3 AKI (85% sensitivity and 67% specificity for a 50.66 ng/mL cutoff). We identified sequential organ failure assessment (SOFA) score (without renal parameters) at admission as an independent factor for developing stages 2 and 3 AKI, and haemoglobin as a protective factor. We observed that metastatic disease, dobutamine use and stage 3 AKI were independent factors associated with 6-month mortality. CONCLUSIONS: In our cohort of critically ill cancer patients, NGAL did not predict AKI. SOFA score was a risk factor for developing AKI, and haemoglobin level was a protective factor for developing AKI. The independent factors associated with 6-month mortality included metastatic disease, dobutamine use, lactate and stage 3 AKI.