The relationship between hospital and intensive care unit length of stay*

Kramer, Andrew A.; Zimmerman, Jack E.

doi:10.1097/ccm.0b013e31820eabab

Cited by 30 publications

(28 citation statements)

References 40 publications

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“…As opposed to the volume-outcome literature [70], mixed models would appear to have been have been applied in a relatively small number of studies for length of stay analysis [16,71-75]. The use of administrative data [5,16] may also be associated with integer based calendar–day recording which lacks the accuracy of fractional day based “exact” times [10,71]. …”

Section: Discussionmentioning

confidence: 99%

“…Because of the non-normality of length of stay, formal trimming [76,77] or truncation [71,77,78] of the data, or deleting [5,73,79] “outliers” has been undertaken prior to possible data transformation. For instance, studies implementing the APACHE III [78] or IV [71] algorithms for predicting ICU length of stay have truncated the latter at 30 days, at a 1% data level; the same fraction as seen with data deletions [73].…”

Section: Discussionmentioning

confidence: 99%

“…For instance, studies implementing the APACHE III [78] or IV [71] algorithms for predicting ICU length of stay have truncated the latter at 30 days, at a 1% data level; the same fraction as seen with data deletions [73]. The current study used a deletion fraction of 0.001%.…”

Section: Discussionmentioning

confidence: 99%

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A review of statistical estimators for risk-adjusted length of stay: analysis of the Australian and new Zealand intensive care adult patient data-base, 2008–2009

Moran

Solomon

2012

BMC Med Res Methodol

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BackgroundFor the analysis of length-of-stay (LOS) data, which is characteristically right-skewed, a number of statistical estimators have been proposed as alternatives to the traditional ordinary least squares (OLS) regression with log dependent variable.MethodsUsing a cohort of patients identified in the Australian and New Zealand Intensive Care Society Adult Patient Database, 2008–2009, 12 different methods were used for estimation of intensive care (ICU) length of stay. These encompassed risk-adjusted regression analysis of firstly: log LOS using OLS, linear mixed model [LMM], treatment effects, skew-normal and skew-t models; and secondly: unmodified (raw) LOS via OLS, generalised linear models [GLMs] with log-link and 4 different distributions [Poisson, gamma, negative binomial and inverse-Gaussian], extended estimating equations [EEE] and a finite mixture model including a gamma distribution. A fixed covariate list and ICU-site clustering with robust variance were utilised for model fitting with split-sample determination (80%) and validation (20%) data sets, and model simulation was undertaken to establish over-fitting (Copas test). Indices of model specification using Bayesian information criterion [BIC: lower values preferred] and residual analysis as well as predictive performance (R2, concordance correlation coefficient (CCC), mean absolute error [MAE]) were established for each estimator.ResultsThe data-set consisted of 111663 patients from 131 ICUs; with mean(SD) age 60.6(18.8) years, 43.0% were female, 40.7% were mechanically ventilated and ICU mortality was 7.8%. ICU length-of-stay was 3.4(5.1) (median 1.8, range (0.17-60)) days and demonstrated marked kurtosis and right skew (29.4 and 4.4 respectively). BIC showed considerable spread, from a maximum of 509801 (OLS-raw scale) to a minimum of 210286 (LMM). R2 ranged from 0.22 (LMM) to 0.17 and the CCC from 0.334 (LMM) to 0.149, with MAE 2.2-2.4. Superior residual behaviour was established for the log-scale estimators. There was a general tendency for over-prediction (negative residuals) and for over-fitting, the exception being the GLM negative binomial estimator. The mean-variance function was best approximated by a quadratic function, consistent with log-scale estimation; the link function was estimated (EEE) as 0.152(0.019, 0.285), consistent with a fractional-root function.ConclusionsFor ICU length of stay, log-scale estimation, in particular the LMM, appeared to be the most consistently performing estimator(s). Neither the GLM variants nor the skew-regression estimators dominated.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

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A review of statistical estimators for risk-adjusted length of stay: analysis of the Australian and new Zealand intensive care adult patient data-base, 2008–2009

Moran

Solomon

2012

BMC Med Res Methodol

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“…Hospital resource usage variables included ICU and hospital (non-ICU) lengths of stay. 38,39 Number of medical consultations was considered a resource usage variable to assess "human" resources and included allergy/immunology, anesthesiology, cardiology, head and neck, emergency medicine, family practice, infectious disease, internal medicine, neurology, neurosurgery, obstetrics and gynecology, oral and maxillofacial surgery, ophthalmology, orthopedics, otolaryngology, psychiatry, pulmonary, radiology, renal service, surgery (general, handorthopedics, thoracic, trauma, vascular), urology, and other. Number of procedures was included to assess human and material resources; all procedures performed during the course of care were included (eg, surgical, diagnostic).…”

Section: Study Variablesmentioning

confidence: 99%

Association of Injury Factors, Not Body Mass Index, With Hospital Resource Usage in Trauma Patients

Lee

Hervey

Berg

et al. 2016

American Journal of Critical Care

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Background Allocating resources appropriately requires knowing whether obese patients use more resources during a hospital stay than nonobese patients. Objectives To determine if trauma patients with different body mass indexes differed in use of resources measured as a multifaceted outcome variable. Methods A trauma registry was used for a retrospective study of adult patients admitted to a midwestern level I trauma center. Patients were stratified into 3 groups: nonobese (normal weight, overweight), obese, and morbidly obese. Three canonical correlation analyses were used to determine the relationship between patient/injury characteristics and hospital resource usage. Results In a sample of 9771 patients, 71.2% were non-obese, 23.8% obese, and 5.0% morbidly obese. For patient/injury characteristics, Injury Severity Score and physiological complications were significant variables for all 3 groups. Scores on the Glasgow Coma Scale were significant for nonobese patients only. For resource usage, intensive care unit length of stay and procedures were significant variables for all 3 groups. Conclusions Associations between body mass index and outcomes have been noted when assessed as independent variables. However, when resource usage was assessed as a multifaceted outcome variable, injury factors (higher Injury Severity Score, lower scores on the Glasgow Coma Scale, more physiological complications) were associated with resource usage (increased length of stay in the intensive care unit and increased number of procedures). These findings provide clinicians a new perspective for evaluating the complex relationship between patient/injury characteristics and hospital resource usage.

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“…A prolonged ICU stay can adversely affect one's health status by increasing the risk of infection, complications and, possibly, mortality [2,3]. It has been demonstrated that LOS in an ICU is affected by several medical, social, psychological and institutional factors [4].…”

Section: Length Of Stay (Los)mentioning

confidence: 99%

The differences between two selected intensive care units located in central and northern Europe — preliminary observation

Adamski

Goraj

Onichimowski

et al. 2015

Anaesthesiol Intensive Ther

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Background: The aim of this study was to evaluate possible differences in the functioning of two selected intensive care units in Poland and Finland. The activity of the units was analysed over a period of one year. Methods:The following parameters were compared: demography of treated populations, site of admission, category of illness, severity of illness (APACHE-II scale), mean length of stay, demanded workload (TISS-28 scale), mortality (both ICU and hospital) and standardized mortality ratio (SMR). Results:The results of this study indicated that most of the patients in the Polish ICU, regardless of age, diagnosis and APACHE II score, presented significantly longer lengths of stay (14.65 ± 13.6 vs 4.1 ± 4.7 days, P = 0.0001), higher mean TISS-28 score (38.9 ± 9.1 vs 31.2 ± 6.1, P = 0.0001) and higher ICU and hospital mortality (41.5% vs 10.2% and 44.7% vs 21.8%, respectively, P = 0.0001). The values of SMR were 0.9 and 0.85 for the Finnish and Polish ICUs, respectively. Conclusion: The collected data indicate huge differences in the utilisation of critical care resources. Treatment in Polish ICU is concentrated on much more severely ill patients which might be sometimes accompanied by futility of care. In order to verify and correctly interpret the presented phenomena, further studies are needed.

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The relationship between hospital and intensive care unit length of stay*

Abstract: Case-mix-adjusted benchmarks for hospital and intensive care unit stays reveal substantial differences in unit efficiency. Hospital and intensive care unit stays are strongly correlated at the patient and unit level, suggesting that there are causal factors common to both.

Cited by 30 publications

References 40 publications

A review of statistical estimators for risk-adjusted length of stay: analysis of the Australian and new Zealand intensive care adult patient data-base, 2008–2009

A review of statistical estimators for risk-adjusted length of stay: analysis of the Australian and new Zealand intensive care adult patient data-base, 2008–2009

Association of Injury Factors, Not Body Mass Index, With Hospital Resource Usage in Trauma Patients

The differences between two selected intensive care units located in central and northern Europe — preliminary observation

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