External validation of models for predicting pneumonia after cardiac surgery.

BACKGROUND: Approximately 20% of patients become infected after cardiac surgery. Pneumonia is one of the most serious infections, increasing the chance of death 14-fold. The higher frequency of pneumonia after cardiac surgery may be explained by surgical conditions. Focusing on high-risk groups may make several strategies more effective, and external validation is an essential phase of building prediction models to identify such groups. PURPOSE: To compare the performance of two previously validated prediction models for pneumonia after cardiac surgery (classification and regression tree [CART] and a logistic regression model [LRM]) on an external validation set. METHODS: A series of 527 adult cardiac surgery patients at a small private hospital were analyzed prospectively to identify prognostic factors for pneumonia. Pneumonia occurred in 7.6% of patients in this derivation set. The probability of pneumonia onset was estimated by means of CART and LRM using a cut-off point that maximized both sensitivity and specificity without decreasing accuracy greatly. The results were confirmed with a validation set obtained by enrollment of consecutive 333 adult patients undergoing major cardiac surgery. There were significant differences in the fraction of emergency cases in the derivation and validation sets. RESULTS: The LRM selected emergency surgery (odds ratio [OR] 5.28), chronic obstructive pulmonary disease (COPD)(OR 4.29), ventricular dysfunction (OR 2.68), and age (OR 1.04) as independent predictors of pneumonia. The CART model selected emergency surgery, age, unstable angina, body mass index, COPD, weight, and ventricular dysfunction as predictors. The CART model also selected low body mass index, weight, and unstable angina as predictors. Emergency surgery was the strongest predictor in both models. The LRM performed better than the CART model for the global, discrimination, and calibration measures. CONCLUSION: The LRM model displayed superior performance. A possible advantage of the CART prediction model is that it may be easier to interpret via its graphical presentation than prediction models based on logistic regression. However, there are a number of disadvantages of the CART approach. The LRM model can be used by infection control practitioners for risk adjustment across different periods or units and for evaluation of the efficacy of new technologies.

A first postoperative day predictive score of mortality for cardiac surgery.

PURPOSE: Several prognostic scores for cardiac surgery based on preoperative variables are available. We propose a new one based on pre-and intraoperative and first postoperative day variables for cardiac surgery patients admitted to a surgical intensive care unit. MATERIALS AND METHODS: Classical cohort of data consecutively collected from June 2000 to March 2003 (1,458 patients). Forty-six risk variables were identified. The statistical study comprised univariate analysis followed by logistic regression with receiver operating characteristics (ROC) curve. RESULTS: After logistic regression, the selected variables and respective odds ratios were: age >65 and <75 years (2.05); age >/=75 years (4.79); left atrial diameter >45 mm (2.58); preoperative creatinine >2 mg/dL (4.84); and cardiopulmonary bypass time >/=180 min (4.93+/-2). The first postoperative day variables were as follows: the worst PaO(2)/FiO(2) <100 (9.47); epinephrine or norepinephrine dose >/=0.1 microg/kg/min (6.78); and mechanical ventilation time >12 h (2.24). The area under the ROC curve was 0.84. CONCLUSION: The score shows the strength of first postoperative day variables, probably related to intraoperative conditions. It also evidences the importance of left atrial diameter as a new marker of preoperative risk.

Rioescore: escore preditivo de mortalidade para pacientes submetidos à cirurgia cardíaca baseado em variáveis de pré, per e primeiro dia de pós-operatório / Rioescore: a predictive score of mortality for patients undergoing cardiac surgery based on pre, intra and first postoperative day variables

Objetivo: Criar um escore preditivo de mortalidade hospitalar em pacientes submetidos à cirurgia carddíaca e admitidos em duas Unidades de Terapia Intensiva Cirúrgica, analisando variáveis pré, per e de primeiro dia de pós-operatório.Métodos: Coorte clássica de 1458 pacientes internados consecutivamente no período de junho 2000 até março de 2003, com 46 variáveis previamente definidas. a análise estatística consistiu em análise univariada, seguida de regressão logística (RL), com teste de tendência linear e curva ROC.Resultados:Após, as variáveis selecionadas com os respectivos OR foram: idade entre 65 e 75 anos (2,05);idade maior ou igual 75 anos (4,79); diâmetro do átrio esquerdo maior 45mm (2,58)...