How Dangerous is a Day in Hospital?

Hauck, Katharina; Zhao, Xueyan

doi:10.1097/mlr.0b013e31822efb09

Cited by 195 publications

(65 citation statements)

References 36 publications

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“…Age was associated with LOS in the majority of previous studies of LSS [41-43] and other orthopedic surgeries [10, 12, 14]. Severity of illness also affects the patient's progress toward recovery and requires a greater need for clinical supervision, and therefore, a longer stay at the hospital.…”

Section: Discussionmentioning

confidence: 98%

Structural equation model analysis of the length-of-hospital stay after lumbar spine surgery

et al. 2015

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Background context Length of hospital stay (LOS) after lumbar spine surgery (LSS) can be affected by many factors. However, few studies have evaluated predictors of LOS, and all have used limited number of variables as predictors. Purpose To identify pre-surgical, surgical, and post-surgical predictors of LOS following LSS. Study Design/Setting Retrospective review of consecutive patients who had LSS at the (Blinded) Hospital from October, 2008 to April, 2012. Patient Sample 593 patients who underwent LSS consisting of laminotomy, laminectomy, or arthrodesis. Outcome Measures Dependent variable: LOS. Multiple pre-surgical, surgical, and post-surgical variables were extracted from patients’ medical records and considered as possible predictors (independent variables) of LOS. Methods Potential predictors that were significantly correlated with LOS were used as indicators to construct three latent factors; pre-surgical, surgical, and post-surgical, which were in turn used to predict LOS in a structural equation model (SEM). Results The average LOS was 4.01±2.73 days. The pre-surgical factor was indicated by age (61.97±14.49 years), prior level of function (60.5% were totally independent), prior hemoglobin level (13.70±1.36 mg/dl), and use of assistive devices (60% were assistive device users). The surgical factor was indicated by severity of illness (50.2% had minor disease severity), presence of complications (1.9%), and stay in an intensive care unit (4.0%). The post-surgical factor was indicated by post-surgical walking distance (166.43±175.75 feet), level of assistance during walking (5.18±0.81 out of 7 points), balance scores (6.18 ±1.82 out of 10 points), and bed mobility and transfer dependency scores (9.81± 1.99 out of 14 points). These three latent factors explained 47% of variation in LOS. Conclusion Post-surgical factors predicted the highest variation in LOS in comparison to pre-surgical and surgical factors and should be taken into consideration for discharge planning. Post-surgical factors are related to the patient's function, modifiable with rehabilitation and can be improved to shorten LOS. Inclusion of more reliable and standardized pre-surgical variables could improve the predictability of the model.

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Section: Discussionmentioning

confidence: 98%

Structural equation model analysis of the length-of-hospital stay after lumbar spine surgery

et al. 2015

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“…Some work has explored the risk of harm to a patient following exposure to an average day in hospitalization [10,18]. Combining models which predict time-varying risks associated with hospitalization with traditional disease-specific prediction rules should theoretically result in much more accurate predictive tools which can be used to update risk estimates as an admission progresses over time.…”

Section: Introductionmentioning

confidence: 99%

Predicting the cumulative risk of death during hospitalization by modeling weekend, weekday and diurnal mortality risks

Coiera

Wang

Magrabi

et al. 2014

BMC Health Serv Res

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BackgroundCurrent prognostic models factor in patient and disease specific variables but do not consider cumulative risks of hospitalization over time. We developed risk models of the likelihood of death associated with cumulative exposure to hospitalization, based on time-varying risks of hospitalization over any given day, as well as day of the week. Model performance was evaluated alone, and in combination with simple disease-specific models.MethodPatients admitted between 2000 and 2006 from 501 public and private hospitals in NSW, Australia were used for training and 2007 data for evaluation. The impact of hospital care delivered over different days of the week and or times of the day was modeled by separating hospitalization risk into 21 separate time periods (morning, day, night across the days of the week). Three models were developed to predict death up to 7-days post-discharge: 1/a simple background risk model using age, gender; 2/a time-varying risk model for exposure to hospitalization (admission time, days in hospital); 3/disease specific models (Charlson co-morbidity index, DRG). Combining these three generated a full model. Models were evaluated by accuracy, AUC, Akaike and Bayesian information criteria.ResultsThere was a clear diurnal rhythm to hospital mortality in the data set, peaking in the evening, as well as the well-known ‘weekend-effect’ where mortality peaks with weekend admissions. Individual models had modest performance on the test data set (AUC 0.71, 0.79 and 0.79 respectively). The combined model which included time-varying risk however yielded an average AUC of 0.92. This model performed best for stays up to 7-days (93% of admissions), peaking at days 3 to 5 (AUC 0.94).ConclusionsRisks of hospitalization vary not just with the day of the week but also time of the day, and can be used to make predictions about the cumulative risk of death associated with an individual’s hospitalization. Combining disease specific models with such time varying- estimates appears to result in robust predictive performance. Such risk exposure models should find utility both in enhancing standard prognostic models as well as estimating the risk of continuation of hospitalization.

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“…1 Increased LOS is also a risk factor for adverse events. 2,3 Moreover, many factors used to measure healthcare quality have been linked to prolonged LOS, 4–7 and LOS has also been used to measure quality. 8–10 In addition, LOS has been used to measure efficiency in hospitals.…”

mentioning

confidence: 99%

Hospital Clostridium difficile Infection Rates and Prediction of Length of Stay in Patients Without C. difficile Infection

Polgreen

Cavanaugh

Polgreen

2016

Infect. Control Hosp. Epidemiol.

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Background Inpatient length of stay (LOS) has been used as a measure of hospital quality and efficiency. Patients with Clostridium difficile infections (CDI) have longer LOS. Objective To describe the relationship between hospital CDI incidence and the LOS of patients without CDI. Design Retrospective cohort analysis. Methods We predicted average LOS for patients without CDI at both the hospital and patient level using hospital CDI incidence. We also controlled for hospital characteristics (eg, bed size) and patient characteristics (eg, comorbidities, age). Setting Healthcare Cost and Utilization Project Nationwide Inpatient Sample, 2009–2011. Patients The Nationwide Inpatient Sample includes patients from a 20% sample of all nonfederal US hospitals. Results Inpatient LOS was significantly longer (P < .001) at hospitals with greater CDI incidence at both the hospital and individual level. At a hospital level, a percentage point increase in the CDI incidence rate was associated with more than an additional day’s stay (between 1.19 and 1.61 days). At the individual level, controlling for all observable variables, a percentage point increase in the CDI incidence rate at their hospital was also associated with longer LOS (between 0.6 and 1.05 additional days). Hospital CDI incidence had a larger impact on LOS than many other commonly used predictors of LOS. Conclusion CDI rates are a predictor of LOS in patients without CDI at an individual and institutional level. CDI rates are easy to measure and report and thus may provide an important marker for hospital efficiency and/or quality.

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How Dangerous is a Day in Hospital?

Cited by 195 publications

References 36 publications

Structural equation model analysis of the length-of-hospital stay after lumbar spine surgery

Structural equation model analysis of the length-of-hospital stay after lumbar spine surgery

Predicting the cumulative risk of death during hospitalization by modeling weekend, weekday and diurnal mortality risks

Hospital Clostridium difficile Infection Rates and Prediction of Length of Stay in Patients Without C. difficile Infection

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