Dataset Information

Measurement Error Due to Patient Flow in Estimates of Intensive Care Unit Length of Stay.

ABSTRACT: Clinical endpoints measured in terms of duration, such as intensive care unit (ICU) length of stay (LOS), are widely used in randomized clinical trials (RCTs) and observational research. In analyses of patient-level data from a recent RCT, in which ICU LOS was the primary endpoint, and in administrative data, we showed that additional ICU time is often accrued by patients after they are deemed ready for discharge. This "immutable" time (which cannot plausibly be altered by interventions under study) varies by day, week, and year, adding on average one-third of a day to total LOS. We then used statistical simulations, informed by the administrative data and RCT, to assess the impact of immutable time on the measurement and statistical comparison of patients' ICU LOS. These simulations demonstrated that immutable time combines with clinically necessary ICU time (neither of which is likely to be normally distributed) to produce overall LOS distributions that might either mask true treatment effects or suggest false treatment effects relative to analyses of time to discharge readiness. The extent and direction of bias were complex functions of the statistical method used, mortality rates and distributions, and the magnitude of immutable time relative to intervention-associated reductions in LOS.

SUBMITTER: Harhay MO

PROVIDER: S-EPMC5860585 | biostudies-literature | 2017 Dec

REPOSITORIES: biostudies-literature

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Measurement Error Due to Patient Flow in Estimates of Intensive Care Unit Length of Stay.

Harhay Michael O MO Ratcliffe Sarah J SJ Halpern Scott D SD

American journal of epidemiology 20171201 12

Clinical endpoints measured in terms of duration, such as intensive care unit (ICU) length of stay (LOS), are widely used in randomized clinical trials (RCTs) and observational research. In analyses of patient-level data from a recent RCT, in which ICU LOS was the primary endpoint, and in administrative data, we showed that additional ICU time is often accrued by patients after they are deemed ready for discharge. This "immutable" time (which cannot plausibly be altered by interventions under st ...[more]

PMID: 28605399

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Project description:Intensive insulin therapy in the critically ill reduces mortality but carries the risk of increased hypoglycemia. Point-of-care blood glucose analysis is standard; however, anemia causes falsely high values and potentially masks hypoglycemia. Permissive anemia is practiced routinely in most intensive care units. We hypothesized that point-of-care glucometer error due to anemia is prevalent, can be corrected mathematically, and correction uncovers occult hypoglycemia during intensive insulin therapy.The study has both retrospective and prospective phases. We reviewed data to verify the presence of systematic error, determine the source of error, and establish the prevalence of anemia. We confirmed our findings by reproducing the error in an in vitro model. Prospective data were used to develop a correction formula validated by the Monte Carlo method. Correction was implemented in a burn intensive care unit and results were evaluated after 9 mos.Burn and trauma intensive care units at a single research institution.Samples for in vitro studies were taken from healthy volunteers. Samples for formula development were from critically ill patients who received intensive insulin therapy.Insulin doses were calculated based on predicted serum glucose values from corrected point-of-care glucometer measurements.Time-matched point-of-care glucose, laboratory glucose, and hematocrit values. We previously found that anemia (hematocrit <34%) produces systematic error in glucometer measurements. The error was correctible with a mathematical formula developed and validated, using prospectively collected data. Error of uncorrected point-of-care glucose ranged from 19% to 29% (p < .001), improving to < or = 5% after mathematical correction of prospective data. Comparison of data pairs before and after correction formula implementation demonstrated a 78% decrease in the prevalence of hypoglycemia in critically ill and anemic patients treated with insulin and tight glucose control (p < .001).A mathematical formula that corrects erroneous point-of-care glucose values due to anemia in intensive care unit patients reduces the prevalence of hypoglycemia during intensive insulin therapy.

Project description:ImportanceLaboratory data are frequently collected throughout the care of critically ill patients. Currently, these data are interpreted by comparison with values from healthy outpatient volunteers. Whether this is the most useful comparison has yet to be demonstrated.ObjectivesTo understand how the distribution of intensive care unit (ICU) laboratory values differs from the reference range, and how these distributions are related to patient outcomes.Design, setting, and participantsCross-sectional study of a large critical care database, the Medical Information Mart for Intensive Care database, from January 1, 2001, to October 31, 2012. The database is collected from ICU data from a large tertiary medical center in Boston, Massachusetts. The data are collected from medical, cardiac, neurologic, and surgical ICUs. All patients in the database from all ICUs for 2001 to 2012 were included. Common laboratory measurements over the time window of interest were sampled. The analysis was conducted from March to June 2017.Main outcomes and measuresThe overlapping coefficient and Cohen standardized mean difference between distributions were calculated, and kernel density estimate visualizations for the association between laboratory values and the probability of death or quartile of ICU length of stay were created.ResultsAmong 38?605 patients in the ICU (21?852 [56.6%] male; mean [SD] age, 74.5 [55.1] years), 8878 (23%) had the best outcome (ICU survival, shortest quartile length of stay) and 3090 (8%) had the worst outcome (ICU nonsurvival). Distribution curves based on ICU data differed significantly from the hospital standard range (mean [SD] overlapping coefficient, 0.51 [0.32-0.69]). All laboratory values for the best outcome group differed significantly from those in the worst outcome group. Both the best and worst outcome group curves revealed little overlap with and marked divergence from the reference range.Conclusions and relevanceThe standard reference ranges obtained from healthy volunteers differ from the analogous range generated from data from patients in intensive care. Laboratory data interpretation may benefit from greater consideration of clinically contextual and outcomes-related factors.

Project description:BackgroundIntensive care unit (ICU) length of stay (LOS) and the risk adjusted equivalent (RALOS) have been used as quality metrics. The latter measures entail either ratio or difference formulations or ICU random effects (RE), which have not been previously compared.MethodsFrom calendar year 2016 data of an adult ICU registry-database (Australia & New Zealand Intensive Care Society (ANZICS) CORE), LOS predictive models were established using linear (LMM) and generalised linear (GLMM) mixed models. Model fixed effects quality-metric formulations were estimated as RALOSR for LMM (geometric mean derived from log(ICU LOS)) and GLMM (day) and observed minus expected ICU LOS (OMELOS from GLMM). Metric confidence intervals (95%CI) were estimated by bootstrapping; random effects (RE) were predicted for LMM and GLMM. Forest-plot displays of ranked quality-metric point-estimates (95%CI) were generated for ICU hospital classifications (metropolitan, private, rural/regional, and tertiary). Robust rank confidence sets (point estimate and 95%CI), both marginal (pertaining to a singular ICU) and simultaneous (pertaining to all ICU differences), were established.ResultsThe ICU cohort was of 94,361 patients from 125 ICUs (metropolitan 16.9%, private 32.8%, rural/regional 6.4%, tertiary 43.8%). Age (mean, SD) was 61.7 (17.5) years; 58.3% were male; APACHE III severity-of-illness score 54.6 (25.7); ICU annual patient volume 1192 (702) and ICU LOS 3.2 (4.9). There was no concordance of ICU ranked model predictions, GLMM versus LMM, nor for the quality metrics used, RALOSR, OMELOS and site-specific RE for each of the ICU hospital classifications. Furthermore, there was no concordance between ICU ranking confidence sets, marginal and simultaneous for models or quality metrics.ConclusionsInference regarding adjusted ICU LOS was dependent upon the statistical estimator and the quality index used to quantify any LOS differences across ICUs. That is, there was no "one best model"; thus, ICU "performance" is determined by model choice and any rankings thereupon should be circumspect.

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Measurement Error Due to Patient Flow in Estimates of Intensive Care Unit Length of Stay.

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Measurement Error Due to Patient Flow in Estimates of Intensive Care Unit Length of Stay.

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