Bayesian Analysis Used to Identify Clinical and Laboratory Variables Capable of Predicting Progression to Severe Dengue among Infected Pediatric Patients.

The current contribution aimed to evaluate the capacity of the naive Bayes classifier to predict the progression of dengue fever to severe infection in children based on a defined set of clinical conditions and laboratory parameters. This case-control study was conducted by reviewing patient files in two public hospitals in an endemic area in Mexico. All 99 qualifying files showed a confirmed diagnosis of dengue. The 32 cases consisted of patients who entered the intensive care unit, while the 67 control patients did not require intensive care. The naive Bayes classifier could identify factors predictive of severe dengue, evidenced by 78% sensitivity, 91% specificity, a positive predictive value of 8.7, a negative predictive value of 0.24, and a global yield of 0.69. The factors that exhibited the greatest predictive capacity in the model were seven clinical conditions (tachycardia, respiratory failure, cold hands and feet, capillary leak leading to the escape of blood plasma, dyspnea, and alterations in consciousness) and three laboratory parameters (hypoalbuminemia, hypoproteinemia, and leukocytosis). Thus, the present model showed a predictive and adaptive capacity in a small pediatric population. It also identified attributes (i.e., hypoalbuminemia and hypoproteinemia) that may strengthen the WHO criteria for predicting progression to severe dengue.

Preoperative passive venous pressure-driven cardiac function determines left ventricular assist device outcomes.

BACKGROUND: Right heart output in heart failure can be compensated through increasing systemic venous pressure. We determined whether the magnitude of this "passive cardiac output" can predict LVAD outcomes. METHODS: This was a retrospective review of 383 patients who received a continuous-flow LVAD at the University of Michigan between 2012 and 2021. Pre-LVAD cardiac output driven by venous pressure was determined by dividing right atrial pressure by mean pulmonary artery pressure, multiplied by total cardiac output. Normalization to body surface area led to the passive cardiac index (PasCI). The Youden J statistic was used to identify the PasCI threshold, which predicted LVAD death by 2 years. RESULTS: Increased preoperative PasCI was associated with reduced survival (hazard ratio [HR], 2.27; P < .01), and increased risk of right ventricular failure (RVF) (HR, 3.46; P = .04). Youden analysis showed that a preoperative PasCI ≥0.5 (n = 226) predicted LVAD death (P = .10). Patients with PasCI ≥0.5 had poorer survival (P = .02), with a trend toward more heart failure readmission days (mean, 45.09 ± 67.64 vs 35.13 ± 45.02 days; P = .084) and increased gastrointestinal bleeding (29.2% vs 20.4%; P = .052). Additionally, of the 97 patients who experienced readmissions for heart failure, those with pre-LVAD implantation PasCI ≥0.5 were more likely to have more than 1 readmission (P = .05). CONCLUSIONS: Although right heart output can be augmented by raising venous pressure, this negatively impacts end-organ function and increases heart failure readmission days. Patients with a pre-LVAD PasCI ≥0.5 have worse post-LVAD survival and increased RVF. Using the PasCI metric in isolation or incorporated into a predictive model may improve the management of LVAD candidates with RV dysfunction.