A quantitative exploration of gastrointestinal bleeding in intensive care unit patients

Background Quantitative assessments of the severity of bleeding in patients with bleeds within the gastrointestinal tract (GIB) are generally limited to blood tests like the hematocrit. The varied and irregular nature of the data collected during such observations makes it difficult in retrospective data analysis to characterize the complete course of bleeding. We intend to quantify the rate of blood loss over the course of an ICU stay, facilitating more precise analysis of retrospective data, and to use this quantification to examine questions about the effects of GIB. Methods and findings A population of 2,445 intensive care admissions across 2,266 patients with a diagnosis of GIB was studied. Using statistical techniques for smoothing data and accepted medical approaches for calculating blood loss, we are able to convert collections of individual laboratory readings that are difficult to understand into a simple, interpretable overview of the patient’s bleeding status over time. To demonstrate this method, we compare patients’ standard vital signs while bleeding heavily to times when they are not bleeding, finding a 3.0 ± 0.5% increase in heart rate, a 1.3 ± 0.4% decrease in systolic blood pressure and a 0.9 ± 0.5% decrease in diastolic blood pressure. After considering the effect of bleeding on standard vital signs, we demonstrate that patients with upper GIB have significantly elevated blood urea nitrogen levels while bleeding heavily, with a mean increase of 11.7 ± 7.2%, while patients with lower GIB do not, with a mean increase of 4.2 ± 6.6%. Conclusions This study introduces a novel method of processing retrospective laboratory data to characterize the course of bleeds within the gastrointestinal tract. This method is used to examine the direct effects of bleeding on a patient and can be deployed in future studies of bleeding using retrospective data

Analysis of factors associated with extended recovery time after colonoscopy

<div><p>Background & aims</p><p>A common limiting factor in the throughput of gastrointestinal endoscopy units is the availability of space for patients to recover post-procedure. This study sought to identify predictors of abnormally long recovery time after colonoscopy performed with procedural sedation. In clinical research, this type of study would be performed using only one regression modeling approach. A goal of this study was to apply various “machine learning” techniques to see if better prediction could be achieved.</p><p>Methods</p><p>Procedural data for 31,442 colonoscopies performed on 29,905 adult patients at Massachusetts General Hospital from 2011 to 2015 were analyzed to identify potential predictors of long recovery times. These data included the identities of hospital personnel, and the initial statistical analysis focused on the impact of these personnel on recovery time via multivariate logistic regression. Secondary analyses included more information on patient vitals both to identify secondary predictors and to predict long recoveries using more complex techniques.</p><p>Results</p><p>In univariate analysis, the endoscopist, procedure room nurse, recovery room nurse, and surgical technician all showed a statistically significant relationship to long recovery times, with p-value below 0.0001 in all cases. In the multivariate logistic regression, the most significant predictor of a long recovery time was the identity of the recovery room nurse, with the endoscopist also showing a statistically significant relationship with a weaker effect. Complex techniques led to a negligible improvement over simple techniques in prediction of long recovery periods.</p><p>Conclusion</p><p>The hospital personnel involved in performing a colonoscopy show a strong association with the likelihood of a patient spending an abnormally long time recovering from the procedure, with the most pronounced effect for the nurse in the recovery room. The application of more advanced approaches to improve prediction in this clinical data set only yielded modest improvements.</p></div

Recovery time averaged by day.

<p>Daily mean recovery time over the study period with a local regression (LOESS) fitted curve.</p

Predictive model results.

<p>Predictive model results.</p

Use of fentanyl and meperidine over time.

<p>Daily percentage of procedures using fentanyl and meperidine over time, with LOESS fitted curves.</p

Histogram of recovery time with cumulative frequency.

<p>Distribution of recovery time among studied population, showing definition of long recovery. For readability, the 71 (1.4%) procedures with recovery time above 200 minutes are excluded from the figure.</p

Procedure characteristics by recovery time.

<p>Procedure characteristics by recovery time.</p

Multivariate regression results.

<p>Multivariate regression results.</p

Recovery time averaged by hospital staff.

<p>Mean recovery time by hospital personnel with 95% confidence interval. Each point represents one individual or the aggregated data of individuals involved in a small number of procedures, as described in the methods section.</p