Length of hospital stay for cerebrovascular disease in the United States: Professional Activity Study, 1963-1991

Which Comorbidities and Complications Predict Ischemic Stroke Recovery and Length of Stay?

OBJECTIVES

Stroke is the second most common cause of death worldwide and can lead to significant disability and long-term costs. Length of stay (LOS) is the most predictive factor in determining inpatient costs. In the present study, factors that affect disability and LOS among ischemic stroke patients admitted to an urban community hospital and 2 university-based teaching hospitals were assessed.

METHODS

Data for consecutive patients with acute ischemic strokes were collected, by reviewing discharge diagnosis International Classification of Diseases codes. A data mining process was used to analyze admission data. Data regarding comorbidities and complications were abstracted by mining the secondary diagnoses for their respective International Classification of Diseases-9 codes. The primary outcome was LOS, calculated from the dates of admission and dates of discharge. The second outcome of interest was disability, which was evaluated by the modified Rankin score at the time of discharge.

RESULTS

LOS progressively increased with greater disability. Greater age and higher National Institute of Health Stroke Scale at admission were associated with both higher disability and longer LOS. Presence of congestive heart failure or chronic kidney disease, atrial fibrillation, other arrhythmias (preexisting or new onset), and development of acute renal failure were associated with greater LOS but not greater disability status. Patients with a previous stroke and those that developed urinary tract infection as a complication had higher disability.

CONCLUSIONS

Greater age and higher National Institute of Health Stroke Scale at admission were associated with both higher disability and longer LOS. Congestive heart failure, CRF, presence of arrhythmias, and development of acute renal failure were associated with greater LOS. The development of urinary tract infection caused higher disability.

Has variation in length of stay in acute hospitals decreased? Analysing trends in the variation in LOS between and within Dutch hospitals

Background

We aimed to get better insight into the development of the variation in length of stay (LOS) between and within hospitals over time, in order to assess the room for efficiency improvement in hospital care.

Methods

Using Dutch national individual patient-level hospital admission data, we studied LOS for patients in nine groups of diagnoses and procedures between 1995 and 2010. We fitted linear mixed effects models to the log-transformed LOS to disentangle within and between hospital variation and to evaluate trends, adjusted for case-mix.

Results

We found substantial differences between diagnoses and procedures in LOS variation and development over time, supporting our disease-specific approach. For none of the diagnoses, relative variance decreased on the log scale, suggesting room for further LOS reduction. Except for two procedures in the same specialty, LOS of individual hospitals did not correlate between diagnoses/procedures, indicating the absence of a hospital wide policy. We found within-hospital variance to be many times greater than between-hospital variance. This resulted in overlapping confidence intervals across most hospitals for individual hospitals’ performances in terms of LOS.

Conclusions

The results suggest room for efficiency improvement implying lower costs per patient treated. It further implies a possibility to raise the number of patients treated using the same capacity or to downsize the capacity. Furthermore, policymakers and health care purchasers should take into account statistical uncertainty when benchmarking LOS between hospitals and identifying inefficient hospitals.

Electronic supplementary material

The online version of this article (doi:10.1186/s12913-015-1087-6) contains supplementary material, which is available to authorized users.

Determinants of the length of stay in stroke patients

OBJECTIVES

The study objective was to identify the factors that influence the length of stay (LOS) in hospital for stroke patients and to provide data for managing hospital costs by managing the LOS.

METHODS

This study used data from the Discharge Injury Survey of the Korea Centers for Disease Control and Prevention, which included 17,364 cases from 2005 to 2008.

RESULT

The LOS for stroke, cerebral infarction, intracerebral hemorrhage, and subarachnoid hemorrhage was 18.6, 15.0, 28.9, and 25.3 days, respectively. Patients who underwent surgery had longer LOS. When patients were divided based on whether they had surgery, there was a 2.4-time difference in the LOS for patients with subarachnoid hemorrhage, 2.0-time difference for patients with cerebral infarction, and 1.4-time difference for patients with intracerebral hemorrhage. The emergency route of admission and other diagnosis increased LOS, whereas hypertension and diabetic mellitus reduced LOS.

CONCLUSION

In the present rapidly changing hospital environments, hospitals approach an efficient policy for LOS, to maintain their revenues and quality of assessment. If LOS is used as the indicator of treatment expenses, there is a need to tackle factors that influence the LOS of stroke patients for each disease group who are divided based on whether surgery is performed or not for the proper management of the LOS.

Model-based prediction of length of stay for rehabilitating stroke patients

BACKGROUND/PURPOSE

Accurate length-of-stay (LOS) estimates have an impact on medical costs for stroke patients. Most studies have reported only descriptive sample means or have provided linear-model-based estimates for LOS. This study calculated point and interval estimates by treating hospital discharge as an event, and utilizing the proportional hazards (PH) model to provide the estimation of hospital stay for first-ever stroke patients in a rehabilitation department of a clinical center.

METHODS

Pairwise analysis for correlations between age, sex, comorbidity status, modified Barthel index (MBI) and functional independence measure (FIM) was performed. These explanatory variables are used in the K-sample comparisons, the Chi-squared test for association, the PH regression analysis, and log-transformed linear (LTL) regression.

RESULTS

The PH model gave a prediction on estimated mean LOS, with an absolute bias of 0.85 days, by combining MBI and FIM into a single variable, or a bias of 1.15 days and 1.16 days with MBI and FIM variables, respectively. The LTL-based estimation generated a bias of 5.91 days. The PH model has relatively shorter confidence intervals than those obtained by sample-mean and LTL methods.

CONCLUSION

We recommend using the PH model for predicting mean LOS when the PH assumption for patients with different clinical characteristics is satisfied. However, the proposed method only applies to rehabilitating stroke patients.

Predicting the cost of hospital stay for stroke patients: the use of diagnosis related groups

In order to provide tailor-made care, governments are considering the implementation of output-pricing based on hospital case-mix measures, such as diagnosis related groups (DRG). The question is whether the current DRG classification system can provide a satisfactory prediction of the variance of costs in stroke patients and if not, in what way other variables may enhance this prediction. In this study, data from 731 stroke patients hospitalized at University Hospital Maastricht during 1996-1998 are used in the cost analysis. The DRG classification for this group uses information--in addition to the DRG classification operation or no operation--on the patient's age combined with discharge status. The results of regression analysis show that using DRGs, the variance explained in the costs amounts to 34%. Adding other variables to the DRGs, the variance explained increases to about 61%. Additional factors highly correlating with inpatient costs are the level of functioning after stroke, comorbidity, complications, and 'days of stay for non-medical reasons'. Costs decreased for stroke patients discharged during the latter part of the years studied, and if stroke patients happened to die during their hospital stay. The results do suggest that future implementation of output-pricing based on the DRG case-mix measures is feasible for stroke patients only if it is enhanced with information on complications and the level of functioning.

Trend of stroke hospitalization, United States, 1988-1997

BACKGROUND AND PURPOSE

Age-adjusted stroke mortality in the United States has declined in recent decades. However, the course of stroke incidence is less certain. To address this issue, we determined trends of stroke hospitalization and in-hospital case fatality during 1988-1997.

METHODS

Stroke hospitalization was estimated from National Hospital Discharge Survey as numerator and Current Population Survey as denominator. Hospitalization rates were determined and stratified by patient characteristics. Average length of hospital stay was also determined. In-hospital mortality was specified by sex, age, and other patient characteristics. The change in these rates over 10 years and average annual percent changes were calculated.

RESULTS

During 1988-1997, age-adjusted stroke hospitalization rate increased 18.6% (from 560 to 664/100 000; P=0.043), while total hospitalization increased from 592 811 to 821 760. This increase was limited to persons aged >/=65 years. Patients in the South had the highest stroke hospitalization rates, and those in the West had the lowest. Overall, 58% of strokes were classified as ischemic, 13% as hemorrhagic, and 29% as other. Over these 10 years, stroke patients having coincident diabetes, hypertension, and congestive heart failure increased 17.4% (P=0.17), 34% (P=0.05), and 31% (P=0.091), respectively. The average length of hospital stay fell from 11.1 to 6.2 days (44.1%; P=0.012). As a result, despite an increase in hospitalizations for stroke, the total person-days in hospital actually decreased by 22% (P=0.06).

CONCLUSIONS

The declining age-adjusted stroke mortality in the United States has not been accompanied by a fall in hospitalization over recent years. Thus far, however, decrease in length of stay has more than offset increased admission. At the same time, the sharp drop in hospital case fatality rates suggests that continuing decline in stroke mortality may be due, in large part, to improved survival after acute stroke.

Poor prognosis of acute stroke patients denied thrombolysis due to early CT findings

OBJECTIVES

Whether acute stroke patients with major early infarct signs on computed tomography (CT) should be treated with intravenous (i.v.) thrombolysis remains controversial. The authors sought to define the outcomes in 5 consecutive patients who were not treated with i.v. thrombolysis, according to established guidelines.

METHODS

The authors retrospectively analyzed the outcomes of a consecutive series of 5 patients evaluated by an acute stroke team at a university medical center and who were denied i.v. tissue plasminogen activator due to early CT changes.

RESULTS

Five patients with a median National Institutes of Health Stroke Scale score of 22 (range 20-28) were evaluated. Despite aggressive care (e.g., hemicraniectomy), 2 patients died owing to herniation, 1 patient died of cardiac causes, and neither of the 2 surviving patients achieved a 3-month Rankin score below 4 (moderately severe disability).

CONCLUSIONS

Given the poor prognosis of patients with hemispheric stroke and early CT changes, alternative treatment modalities such as intra-arterial thrombolysis, early hemicraniectomy, and neuroprotective therapy should be vigorously pursued.

Preventable inpatient time: adequacy of electronic patient information systems

OBJECTIVES

This study assessed hospital electronic patient information systems (EPIS) for inclusion of variables associated with avoidable and extended hospitalization (preventable inpatient time).

METHODS

We searched MEDLINE and HealthSTAR databases to identify predictors of preventable inpatient time. We then audited the admissions process and the handwritten medical record at 1 hospital, and the EPIS at all hospitals, affiliated with the Yale University School of Medicine for inclusion of the predictors.

RESULTS

Whereas the written medical record included all 58 predictors, the EPIS of the 10 hospitals surveyed included an average of only 38% of the predictors.

CONCLUSIONS

The conventional approach to information gathering during hospital admission is highly inefficient. Revising EPIS to include predictors of preventable inpatient time could enhance efficiency and quality, while reducing costs, of hospital care.

Results of a computerized screening of stroke patients for unjustified hospital stay

BACKGROUND AND PURPOSE

Effective methods to monitor length of stay can help reduce unnecessary hospital stay without adversely affecting the quality of care. In this study a clinical algorithm for assessing unjustified hospital stay in stroke patients was computerized and tested.

METHODS

An algorithm was developed by the authors to estimate the number of medically justified and unjustified hospital days for patients admitted with a primary diagnosis of ischemic stroke. Data for the algorithm were obtained from 177 stroke patients from an acute-care teaching hospital. The performance of the algorithm was evaluated on a subset of 46 patients by comparing the number of medically unjustified hospital days determined by the algorithm with the consensus determination of two neurologists.

RESULTS

The algorithm classified 68% of the 177 patients as having some unjustified hospital days and 41% of all hospital days as unjustified. With the neurologists as the gold standard, the sensitivity of the algorithm was .89 and the specificity was .91. The correlation between the number of unjustified days determined by the algorithm and the neurologists was .76.

CONCLUSIONS

There is considerable unjustified length of stay for stroke patients. Physicians can develop simple clinical algorithms for detecting unjustified hospital stay in stroke patients that provide a reasonable approximation of complex clinical judgment.