Evolution of Investigating Informed Assent Discussions about CPR in Seriously Ill Patients.

CONTEXT: Outcomes after cardiopulmonary resuscitation (CPR) remain poor. We have spent 10 years investigating an "informed assent" (IA) approach to discussing CPR with chronically ill patients/families. IA is a discussion framework whereby patients extremely unlikely to benefit from CPR are informed that unless they disagree, CPR will not be performed because it will not help achieve their goals, thus removing the burden of decision-making from the patient/family, while they retain an opportunity to disagree. OBJECTIVES: Determine the acceptability and efficacy of IA discussions about CPR with older chronically ill patients/families. METHODS: This multi-site research occurred in three stages. Stage I determined acceptability of the intervention through focus groups of patients with advanced COPD or malignancy, family members, and physicians. Stage II was an ambulatory pilot randomized controlled trial (RCT) of the IA discussion. Stage III is an ongoing phase 2 RCT of IA versus attention control in in patients with advanced chronic illness. RESULTS: Our qualitative work found the IA approach was acceptable to most patients, families, and physicians. The pilot RCT demonstrated feasibility and showed an increase in participants in the intervention group changing from "full code" to "do not resuscitate" within two weeks after the intervention. However, Stages I and II found that IA is best suited to inpatients. Our phase 2 RCT in older hospitalized seriously ill patients is ongoing; results are pending. CONCLUSIONS: IA is a feasible and reasonable approach to CPR discussions in selected patient populations.

The utility of nodule volume in the context of malignancy prediction for small pulmonary nodules.

BACKGROUND: An estimated 150,000 pulmonary nodules are identified each year, and the number is likely to increase given the results of the National Lung Screening Trial. Decision tools are needed to help with the management of such pulmonary nodules. We examined whether adding any of three novel functions of nodule volume improves the accuracy of an existing malignancy prediction model of CT scan-detected nodules. METHODS: Swensen's 1997 prediction model was used to estimate the probability of malignancy in CT scan-detected nodules identified from a sample of 221 patients at the Medical University of South Carolina between 2006 and 2010. Three multivariate logistic models that included a novel function of nodule volume were used to investigate the added predictive value. Several measures were used to evaluate model classification performance. RESULTS: With use of a 0.5 cutoff associated with predicted probability, the Swensen model correctly classified 67% of nodules. The three novel models suggested that the addition of nodule volume enhances the ability to correctly predict malignancy; 83%, 88%, and 88% of subjects were correctly classified as having malignant or benign nodules, with significant net improved reclassification for each (P<.0001). All three models also performed well based on Nagelkerke R2, discrimination slope, area under the receiver operating characteristic curve, and Hosmer-Lemeshow calibration test. CONCLUSIONS: The findings demonstrate that the addition of nodule volume to existing malignancy prediction models increases the proportion of nodules correctly classified. This enhanced tool will help clinicians to risk stratify pulmonary nodules more effectively.

Using endobronchial ultrasound features to predict lymph node metastasis in patients with lung cancer.

PURPOSES: Reliable staging of the mediastinum determines TNM classification and directs therapy for non-small cell lung cancer (NSCLC). Our aim was to evaluate predictors of mediastinal lymph node metastasis in patients undergoing endobronchial ultrasound (EBUS). METHODS: Patients with known or suspected lung cancer undergoing EBUS for staging were included. Lymph node radiographic characteristics on chest CT/PET scan and ultrasound characteristics of size, shape, border, echogenicity, and number were correlated with rapid on-site evaluation (ROSE) and final pathology. Logistic regression (estimated with generalized estimating equations to account for correlation across nodes within patients) was used with cancer (vs normal pathology) as the outcome. ORs compare risks across groups, and testing was performed with two-sided α of 0.05. RESULTS: Two hundred twenty-seven distinct lymph nodes (22.5% positive for malignancy) were evaluated in 100 patients. Lymph node size, by CT scan and EBUS measurements, and round and oval shape were predictive of mediastinal metastasis. Increasing size of lymph nodes on EBUS was associated with increasing malignancy risk (P = .0002). When adjusted for CT scan size, hypermetabolic lymph nodes on PET scan did not predict malignancy. Echogenicity and border contour on EBUS and site of biopsy were not significantly associated with cancer. In 94.8% of lymph nodes with a clear diagnosis, the ROSE of the first pass correlated with subsequent passes. CONCLUSIONS: Lymph node size on CT scan and EBUS and round or oval shape by EBUS are predictors of malignancy, but no single characteristic can exclude a visualized lymph node from biopsy. Further, increasing the number of samples taken is unlikely to significantly improve sensitivity.

Influence of nodule detection software on radiologists' confidence in identifying pulmonary nodules with computed tomography.

PURPOSE: With advances in technology, detection of small pulmonary nodules is increasing. Nodule detection software (NDS) has been developed to assist radiologists with pulmonary nodule diagnosis. Although it may increase sensitivity for small nodules, often there is an accompanying increase in false-positive findings. We designed a study to examine the extent to which computed tomography (CT) NDS influences the confidence of radiologists in identifying small pulmonary nodules. MATERIALS AND METHODS: Eight radiologists (readers) with different levels of experience examined thoracic CT scans of 131 cases and identified all the clinically relevant pulmonary nodules. The reference standard was established by an expert, dedicated thoracic radiologist. For each nodule, the readers recorded nodule size, density, location, and confidence level. Two weeks (or more) later, the readers reinterpreted the same scans; however, this time they were provided marks, when present, as indicated by NDS and asked to reassess their level of confidence. The effect of NDS on changes in reader confidence was assessed using multivariable generalized linear regression models. RESULTS: A total of 327 unique nodules were identified. Declines in confidence were significantly (P<0.05) associated with the absence of an NDS mark and smaller nodules (odds ratio=71.0, 95% confidence interval =14.8-339.7). Among nodules with pre-NDS confidence less than 100%, increases in confidence were significantly (P<0.05) associated with the presence of an NDS mark (odds ratio=6.0, 95% confidence interval =2.7-13.6) and larger nodules. Secondary findings showed that NDS did not improve reader diagnostic accuracy. CONCLUSION: Although in this study NDS does not seem to enhance reader accuracy, the confidence of the radiologists in identifying small pulmonary nodules with CT is greatly influenced by NDS.

Imprecision in automated volume measurements of pulmonary nodules and its effect on the level of uncertainty in volume doubling time estimation.

BACKGROUND: Detection of small indeterminate pulmonary nodules (4 to 10 mm in diameter) in clinical practice is increasing, largely because of increased utilization and improved imaging technology. Although there currently exists software for CT scan machines that automate nodule volume estimation, the imprecision associated with volume estimates is particularly poor for nodules < or = 6 mm in diameter, with greater imprecision associated with increasing CT scan slice thickness. This study examined the effects of the volume estimation error associated with four CT scan slice thicknesses (0.625, 1.25, 2.50, and 5.00 mm) on estimates of volume doubling time (VDT) for solid nodules of various sizes. METHODS: Data reflecting the accuracy of 1,624 automated volume estimations were obtained from experiments incorporating volume estimation software, performed on a commercially available lung phantom. These data informed mathematical simulations that were used to estimate imprecision around VDT estimates for hypothetical pairs of volume estimates for a given solid pulmonary nodule observed at different time points. RESULTS: The confidence intervals around the VDT estimates were extremely wide for 2.50- and 5.00-mm slice thicknesses, often encompassing values traditionally associated with both benignity and malignity for simulated 1- and 2-mm growths in diameter. CONCLUSIONS: Because of the inaccuracy in automated volume estimation, the confidence a clinician should have in estimating VDT should be highly dependent on the degree of observed growth and on the CT scan slice thickness. The performance of CT scanners with slice thicknesses of > or = 2.5 mm for assessing growth in pulmonary nodules is essentially inadequate for 1-mm changes in nodule diameter.

Pulmonary nodule volume: effects of reconstruction parameters on automated measurements--a phantom study.

PURPOSE: To prospectively evaluate in a phantom the effects of reconstruction kernel, field of view (FOV), and section thickness on automated measurements of pulmonary nodule volume. MATERIALS AND METHODS: Spherical and lobulated pulmonary nodules 3-15 mm in diameter were placed in a commercially available lung phantom and scanned by using a 16-section computed tomographic (CT) scanner. Nodule volume (V) was determined by using the diameters of 27 spherical nodules and the mass and density values of 29 lobulated nodules measured by using the formulas V = (4/3)pi r(3) (spherical nodules) and V = 1000 x (M/D) (lobulated nodules) as reference standards, where r is nodule radius; M, nodule mass; and D, wax density. Experiments were performed to evaluate seven reconstruction kernels and the independent effects of FOV and section thickness. Automated nodule volume measurements were performed by using computer-assisted volume measurement software. General linear regression models were used to examine the independent effects of each parameter, with percentage overestimation of volume as the dependent variable of interest. RESULTS: There was no substantial difference in the accuracy of volume estimations across the seven reconstruction kernels. The bone reconstruction kernel was deemed optimal on the basis of the results of a series of statistical analyses and other qualitative findings. Overall, volume accuracy was significantly associated (P < .0001) with larger reference standard-measured nodule diameter. There was substantial overestimation of the volumes of the 3-5-mm nodules measured by using the volume measurement software. Decreasing the FOV facilitated no significant improvement in the precision of lobulated nodule volume measurements. The accuracy of volume estimations--particularly those for small nodules--was significantly (P < .0001) affected by section thickness. CONCLUSION: Substantial, highly variable overestimation of volume occurs with decreasing nodule diameter. A section thickness that enables the acquisition of at least three measurements along the z-axis should be used to measure the volumes of larger pulmonary nodules.