Storlie, Curtis B.

Objective: Anemia is common during critical illness and often persists after hospital discharge; however, its potential association with physical outcomes after critical illness is unclear. Our objective was to assess the associations between hemoglobin at intensive care unit (ICU) and hospital discharge with physical status at 3-month follow-up in acute respiratory distress syndrome (ARDS) survivors. Methods: This is a secondary analysis of a multisite prospective cohort study of 195 mechanically ventilated ARDS survivors from 13 ICUs at 4 teaching hospitals in Baltimore, Maryland. Multivariable regression was utilized to assess the relationships between ICU and hospital discharge hemoglobin concentrations with measures of physical status at 3 months, including muscle strength (Medical Research Council sumscore), exercise capacity (6-minute walk distance [6MWD]), and self-reported physical functioning (36-Item Short-Form Health Survey [SF-36v2] Physical Function score and Activities of Daily Living [ADL] dependencies). Results: Median (interquartile range) hemoglobin concentrations at ICU and hospital discharge were 9.5 (8.5-10.7) and 10.0 (9.0-11.2) g/dL, respectively. In multivariable regression analyses, higher ICU discharge hemoglobin concentrations (per 1 g/dL) were associated with greater 3-month 6MWD mean percent of predicted (3.7% [95% confidence interval 0.8%-6.5%]; P = .01) and fewer ADL dependencies (−0.2 [−0.4 to −0.1]; P = .02), but not with percentage of maximal muscle strength (0.7% [−0.9 to 2.3]; P = .37) or SF-36v2 normalized Physical Function scores (0.8 [−0.3 to 1.9]; P = .15). The associations of physical outcomes and hospital discharge hemoglobin concentrations were qualitatively similar, but none were statistically significant. Conclusions: In ARDS survivors, higher hemoglobin concentrations at ICU discharge, but not hospital discharge, were significantly associated with improved exercise capacity and fewer ADL dependencies. Future studies are warranted to further assess these relationships.

OBJECTIVENonhome discharge and unplanned readmissions represent important cost drivers following spinal fusion. The authors sought to utilize different machine learning algorithms to predict discharge to rehabilitation and unplanned readmissions in patients receiving spinal fusion.METHODSThe authors queried the 2012–2013 American College of Surgeons National Surgical Quality Improvement Program (ACS-NSQIP) for patients undergoing cervical or lumbar spinal fusion. Outcomes assessed included discharge to nonhome facility and unplanned readmissions within 30 days after surgery. A total of 7 machine learning algorithms were evaluated. Predictive hierarchical clustering of procedure codes was used to increase model performance. Model performance was evaluated using overall accuracy and area under the receiver operating characteristic curve (AUC), as well as sensitivity, specificity, and positive and negative predictive values. These performance metrics were computed for both the imputed and unimputed (missing values dropped) datasets.RESULTSA total of 59,145 spinal fusion cases were analyzed. The incidence rates of discharge to nonhome facility and 30-day unplanned readmission were 12.6% and 4.5%, respectively. All classification algorithms showed excellent discrimination (AUC > 0.80, range 0.85–0.87) for predicting nonhome discharge. The generalized linear model showed comparable performance to other machine learning algorithms. By comparison, all models showed poorer predictive performance for unplanned readmission, with AUC ranging between 0.63 and 0.66. Better predictive performance was noted with models using imputed data.CONCLUSIONSIn an analysis of patients undergoing spinal fusion, multiple machine learning algorithms were found to reliably predict nonhome discharge with modest performance noted for unplanned readmissions. These results provide early evidence regarding the feasibility of modern machine learning classifiers in predicting these outcomes and serve as possible clinical decision support tools to facilitate shared decision making.

We consider the problem of model‐based clustering in the presence of many correlated, mixed continuous, and discrete variables, some of which may have missing values. Discrete variables are treated with a latent continuous variable approach, and the Dirichlet process is used to construct a mixture model with an unknown number of components. Variable selection is also performed to identify the variables that are most influential for determining cluster membership. The work is motivated by the need to cluster patients thought to potentially have autism spectrum disorder on the basis of many cognitive and/or behavioral test scores. There are a modest number of patients (486) in the data set along with many (55) test score variables (many of which are discrete valued and/or missing). The goal of the work is to (1) cluster these patients into similar groups to help identify those with similar clinical presentation and (2) identify a sparse subset of tests that inform the clusters in order to eliminate unnecessary testing. The proposed approach compares very favorably with other methods via simulation of problems of this type. The results of the autism spectrum disorder analysis suggested 3 clusters to be most likely, while only 4 test scores had high (>0.5) posterior probability of being informative. This will result in much more efficient and informative testing. The need to cluster observations on the basis of many correlated, continuous/discrete variables with missing values is a common problem in the health sciences as well as in many other disciplines.

Importance: Media reports have questioned the safety of overlapping surgical procedures, and national scrutiny has underscored the necessity of single-center evaluations of its safety; however, sample sizes are likely small. We compared the safety profiles of overlapping and nonoverlapping pediatric procedures at a single children's hospital and discussed methodological considerations of the evaluation. Data and Design: Retrospective analysis of inpatient pediatric surgical procedures (January 2013 to September 2015) at a single pediatric referral center. Overlapping and nonoverlapping procedures were matched in an unbalanced manner (m:n) by procedure. Mixed models adjusting for Vizient-predicted risk, case-mix, and surgeon compared inpatient mortality and length of stay (LOS). Results: Among 315 overlapping procedures, 256 (81.3%) were matched to 645 nonoverlapping procedures. There were 6 deaths in all. The adjusted odds ratio for mortality did not differ significantly between nonoverlapping and overlapping procedures (adjusted odds ratio = 0.94 vs overlapping; 95% CI, 0.02–48.5; P = 0.98). Wide confidence intervals were minimally improved with Bayesian methods (95% CI, 0.07–12.5). Adjusted LOS estimates were not clinically different by overlapping status (0.6% longer for nonoverlapping; 95% CI, 9.7% shorter to 12.2% longer; P = 0.91). Among the 87 overlapping procedures with the greatest overlap (≥60 min or ≥50% of operative duration), there were no deaths. Conclusions: The safety of overlapping and nonoverlapping surgical procedures did not differ at this children's center. These findings may not extrapolate to other centers. LOS or intraoperative measures may be more appropriate than mortality for safety evaluations due to low event rates for mortality.