Evaluación del desempeño pronóstico de dos puntajes de predicción de mortalidad a siete días en pacientes adultos oncológicos críticamente enfermos admitidos en una unidad de cuidados intensivos / Prognostic performance assessment of two seven-day mortality prediction scores in oncologic critically ill adult patients admitted to an intensive care unit

Resumen Introducción: los sistemas de puntuación para predicción se han desarrollado para medir la severidad de la enfermedad y el pronóstico de los pacientes en la unidad de cuidados intensivos. Son útiles para la toma de decisiones clínicas, la estandarización de la investigación y la comparación de la calidad de la atención. Material y métodos: estudio observacional analítico de cohorte en el que revisaron las historias clínicas de 283 pacientes oncológicos admitidos en la unidad de cuidados intensivos (UCI) del Centro de Investigaciones Oncológicas CIOSAD, durante enero de 2014 a enero de 2016, a quienes se les estimó la probabilidad de mortalidad con los puntajes pronósticos APACHE IV y MPM II, se realizó regresión logística binaria con las variables de los modelos en sus estudios originales, se determinó calibración, discriminación y se calcularon criterios de información Akaike AIC y Bayesiano BIC. Resultados: en la evaluación de desempeño de los puntajes pronósticos APACHE IV mostró mayor capacidad de predicción (AUC = 0.95) versus MPM II (AUC = 0.78), los dos modelos mostraron adecuada calibración con estadístico de Hosmer y Lemeshow para APACHE IV (p = 0.39) y para MPM II (p = 0.99). El delta BIC es 2.9 mostrando evidencia positiva en contra de APACHE IV. El estadístico AIC es menor para APACHE IV indicando que es el puntaje con mejor ajuste a los datos. Conclusiones: APACHE IV tiene un buen desempeño en la predicción de mortalidad de pacientes oncológicos críticamente enfermos. Es una herramienta útil para el clínico en su labor diaria. al permitirle distinguir los pacientes con alta probabilidad de mortalidad. (Acta Med Colomb 2018; 43: 81-89).

Abstract Introduction: scoring systems for prediction have been developed to measure the severity of the disease and the prognosis of patients in the intensive care unit. They are useful for clinical decision-making, standardizing research and comparing the quality of care. Materials and Methods: an observational cohort analytical study in which the medical records of 283 oncological patients admitted to the intensive care unit (ICU) of the CIOSAD Oncology Research Center from January 2014 to January 2016 were reviewed. The probability of mortality with the APACHE IV and MPM II prognostic scores was estimated, binary logistic regression was performed with the variables of the models in their original studies, calibration, discrimination was determined and Akaike AIC and Bayesian BIC information criteria were calculated. Results: in the evaluation of the performance of the prediction scores, APACHE IV showed greater predictive capacity (AUC = 0.95) versus MPM II (AUC = 0.78); the two models showed adequate calibration with Hosmer and Lemeshow statistics for APACHE IV (p = 0.39) and for MPM II (p = 0.99). The BIC delta is 2.9 showing positive evidence against APACHE IV. The AIC statistic is lower for APACHE IV indicating that it is the score with the best fit to the data. Conclusions: APACHE IV has a good performance in the prediction of mortality of critically ill oncologic patients. It is a useful tool for the clinician in his daily work, allowing him to distinguish patients with a high probability of mortality. (Acta Med Colomb 2018; 43: 81-89).

Correlation between preoperative GPS and postoperative complications and prognosis of patients with colorectal cancer / 实用医学杂志

Objective To investigate the value of GPS score and CEA in predicting the prognosis of pa-tients with colorectal cancer undergoing laparoscopic surgery. Methods 120 patients diagnosed as colorectal can-cer in our hospital were involved and their baseline information include Height,weight,history,complication, course of disease,tumor size,pathological type,tumor location,TNM stage,vascular tumor thrombus and lymph node metastasis were recorded. Then,all of the patients were followed-up 18 months and patients with favorable prognosis were defined as the favorable group while patients with unfavorable prognosis were defined as the unfavor-able group. Cox′s proportional hazard regression model analysis was applied to evaluate the influencing degree of those factors on the prognosis of the subjects. The factors in predicting prognosis were calculated by ROC curves. Results The poor prognosis rate of patients with colorectal cancer after operation treatments was 37.84%. Cox′s proportional hazard regression model analysis showed that CEA(P = 0.035),GPS score(P = 0.023)have influenc-es on the prognosis. Conclusion GPS scores and CEA may assess the prognosis of patients with colorectal cancer undergoing laparoscopic surgery,which is expected to be used as an indicator of predicting the prognosis of pa-tients with colorectal cancer undergoing laparoscopic surgery.

A 6-Point TACS Score Predicts In-Hospital Mortality Following Total Anterior Circulation Stroke

BACKGROUND AND PURPOSE: Little is known about the factors associated with in-hospital mortality following total anterior circulation stroke (TACS). We examined the characteristics and comorbidity data for TACS patients in relation to in-hospital mortality with the aim of developing a simple clinical rule for predicting the acute mortality outcome in TACS. METHODS: A routine data registry of one regional hospital in the UK was analyzed. The subjects were 2,971 stroke patients with TACS (82% ischemic; median age=81 years, interquartile age range=74–86 years) admitted between 1996 and 2012. Uni- and multivariate regression models were used to estimate in-hospital mortality odds ratios for the study covariates. A 6-point TACS scoring system was developed from regression analyses to predict in-hospital mortality as the outcome. RESULTS: Factors associated with in-hospital mortality of TACS were male sex [adjusted odds ratio (AOR)=1.19], age (AOR=4.96 for ≥85 years vs. <65 years), hemorrhagic subtype (AOR=1.70), nonlateralization (AOR=1.75), prestroke disability (AOR=1.73 for moderate disability vs. no symptoms), and congestive heart failure (CHF) (AOR=1.61). Risk stratification using the 6-point TACS Score [T=type (hemorrhage=1 point) and territory (nonlateralization=1 point), A=age (65–84 years=1 point, ≥85 years=2 points), C=CHF (if present=1 point), S=status before stroke (prestroke modified Rankin Scale score of 4 or 5=1 point)] reliably predicted a mortality outcome: score=0, 29.4% mortality; score=1, 46.2% mortality [negative predictive value (NPV)=70.6%, positive predictive value (PPV)=46.2%]; score=2, 64.1% mortality (NPV=70.6, PPV=64.1%); score=3, 73.7% mortality (NPV=70.6%, PPV=73.7%); and score=4 or 5, 81.2% mortality (NPV=70.6%, PPV=81.2%). CONCLUSIONS: We have identified the key determinants of in-hospital mortality following TACS and derived a 6-point TACS Score that can be used to predict the prognosis of particular patients.