Incidental Findings in the Trauma Population: Interdisciplinary Approach and Electronic Medical Record Reminder Association with Pre-Discharge Reporting and Medicolegal Risk.

BACKGROUND: Incidental findings (IFs) are reported in 20% or more of trauma CT scans. In addition to the importance of patient disclosure, there is considerable legal pressure to avoid missed diagnoses. We reported previously that 63.5% of IFs were disclosed before discharge and with 20% were nondisclosed. We initiated a multidisciplinary systemic plan to effect predischarge disclosure by synoptic CT reports with American College of Radiology recommended follow-up, electronic medical records discharge prompts, and provider education. STUDY DESIGN: Prospective observational series patients from November 2019 to February 2020 were included. Statistical analysis was performed with SPSS, version 21 (IBM Corp). RESULTS: Eight hundred and seventy-seven patients underwent 1 or more CT scans for the evaluation of trauma (507 were male and 370 were female). Mean age of the patients was 57 years (range 14 to 99 years) and 96% had blunt injury. In 315 patients, there were 523 IFs (1.7 per patient); the most common were lung (17.5%), kidney (13%), and liver (11%). Radiology report compliance rate was 84% (210 of 249 patients). There were 66 studies from outside facilities. Sixteen IFs were suspicious for malignancy. A total of 151 patients needed no follow-up and 148 patients needed future follow-up evaluation. Predischarge IF disclosure compliance rate was 90.1% (286 patients); 25 were post discharge. Four patients remained undisclosed. Compared with our previous report, clearer reporting and electronic medical records prompts increased predischarge disclosure from 63.5% to 90.1% (p < 0.01, chi-square test) and decreased days to notification from 29.5 (range 0 to 277) to 5.2 (range 0 to 59) (p < 0.01, Mann-Whitney U test). CONCLUSIONS: Timely, complete disclosure of IFs improves patient outcomes and reduces medicolegal risk. Collaboration among trauma, radiology, and information technology promotes improved disclosure in trauma populations.

A population model of integrative cardiovascular physiology.

We present a small integrative model of human cardiovascular physiology. The model is population-based; rather than using best fit parameter values, we used a variant of the Metropolis algorithm to produce distributions for the parameters most associated with model sensitivity. The population is built by sampling from these distributions to create the model coefficients. The resulting models were then subjected to a hemorrhage. The population was separated into those that lost less than 15 mmHg arterial pressure (compensators), and those that lost more (decompensators). The populations were parametrically analyzed to determine baseline conditions correlating with compensation and decompensation. Analysis included single variable correlation, graphical time series analysis, and support vector machine (SVM) classification. Most variables were seen to correlate with propensity for circulatory collapse, but not sufficiently to effect reasonable classification by any single variable. Time series analysis indicated a single significant measure, the stressed blood volume, as predicting collapse in situ, but measurement of this quantity is clinically impossible. SVM uncovered a collection of variables and parameters that, when taken together, provided useful rubrics for classification. Due to the probabilistic origins of the method, multiple classifications were attempted, resulting in an average of 3.5 variables necessary to construct classification. The most common variables used were systemic compliance, baseline baroreceptor signal strength and total peripheral resistance, providing predictive ability exceeding 90%. The methods presented are suitable for use in any deterministic mathematical model.