Scoring systems are a very attractive family of clinical predictive models, because the patient score can be calculated without using any data processing system. Their weakness lies in the difficulty of associating a reliable prognostic probability with each score. In this study a bootstrap approach for estimating confidence intervals of outcome probabilities is described and applied to design and optimize the performance of a scoring system for morbidity in intensive care units after heart surgery.

The first-order model of breathing mechanics is widely used in clinical practice to assess the viscoelastic properties of the respiratory system. Although simple, this model takes the predominant features of the pressure-flow relationship into account but gives highly systematic residuals between measured and model-predicted variables. To achieve a better fit of the entire data set, an approach hypothesizing deterministic time-variations of model parameters, summarized by information-weighted histograms was recently proposed by Bates and Lauzon. The present study uses flow and pressure data measured in intensive care patients to evaluate the real potential of this approach in clinical practice. Information-weighted histograms of the model parameters, estimated by an on-line identification algorithm, were first constructed by taking into account the parameter percentage standard deviations. Then, the influence of the respiratory flow pattern on the calculated histograms was evaluated by the Kolmogorov-Smirnov statistical test. The results show that the method gives good reproducibility under stable experimental conditions. In addition, for a given airflow waveform, an increase in respiratory frequency shifts the histograms representing time-varying viscous properties strongly versus lower values, whereas it shifts the histograms representing time-varying elastic properties slightly versus higher values. On the other hand, the same histograms were highly dependent on the airflow waveform, especially for the viscous properties. Even in a limited experimental work, in all the conditions considered, the method provides results which agree well with the physiological knowledge of nonlinear and multicompartment behavior of respiratory mechanics.

Background and Objectives: Early identification of patients at risk of clinical deterioration is essential for optimizing therapeutic management and improving outcomes in general medicine wards. The National Early Warning Score 2 (NEWS2) is a validated tool for predicting patient worsening but integrating it with additional clinical and demographic data can enhance its predictive accuracy and support timely clinical decisions. Material and methods: In this retrospective cohort study, 2108 patients admitted to the general medicine department of the University Hospital of Siena were analyzed. Logistic regression models incorporating NEWS2 alongside key clinical variables—including age, presence of central venous catheter (CVC), and functional status measured by the Barthel Index—were developed to predict high clinical risk (HCR) and mortality. Model performance was assessed using the area under the ROC curve (AUC). Results: High clinical risk status developed in 29% of patients. Older age, presence of CVC, lower Barthel Index, and higher NEWS2 scores were significantly associated with both HCR and mortality. The integrated predictive model demonstrated good accuracy, with an AUC of 0.798 for HCR and 0.716 for mortality prediction. Conclusions: This study suggests that NEWS2, when combined with additional patient-specific variables from the electronic health record, can become a more sophisticated tool for early risk stratification. Such a tool has the potential to support timely clinical intervention and optimized therapeutic management, potentially contributing to improved patient outcomes. While the model may indirectly support nurse workload balancing by identifying patients requiring intensified care, its ultimate impact on patient outcomes requires confirmation through prospective studies.

Length-of-stay prediction for cardiac surgery patients is a key point for medical management issues, such as optimization of resources in intensive care units and operating room scheduling. Scoring systems are a very attractive family of predictive models, but their retraining and updating are generally critical. The present approach to designing a scoring system for predicting length of stay in intensive care aims to overcome these difficulties, so that a model designed in a given scenario can easily be adjusted over time or for internal purposes. A naïve Bayes approach was used to develop a simple scoring system. A set of 36 preoperative, intraoperative and postoperative variables collected in a sample of 3256 consecutive adult patients undergoing heart surgery were considered as likely risk predictors. The number of variables was reduced by selecting an optimal subset of features. Scoring system performance was assessed by cross-validation. After the selection process, seven variables were entered in the prediction model, which showed excellent discrimination, good generalization power and suitable sensitivity and specificity. No significant difference was found between AUC of the training and testing sets. The 95% confidence interval for AUC estimated by the BCa bootstrap method was [0.841, 0.883] and [0.837, 0.880] in the training and testing sets, respectively. Chronic dialysis, low postoperative cardiac output and acute myocardial infarction proved to be the major risk factors. The proposed approach produced a simple and trustworthy scoring system, which is easy to update regularly and to customize for other centers. This is a crucial point when scoring systems are used as predictive models in clinical practice.