Impact on mortality of increasing surgical volumes within hospitals after regionalization of thoracic surgery in Ontario, Canada.

2012 ◽  
Vol 30 (34_suppl) ◽  
pp. 225-225
Author(s):  
Anna Bendzsak ◽  
Nancy N. Baxter ◽  
Gail Darling ◽  
Peter Austin ◽  
David R. Urbach
Keyword(s):  
Length Of Stay ◽  
Hospital Volume ◽  
Negative Binomial ◽  
Lung Resection ◽  
Negative Binomial Regression ◽  
Post Discharge ◽  
Theory Of Practice ◽  
Outcome Relationship ◽  
Volume Outcome ◽  
The Difference

225 Background: The causal direction of the volume-outcome relationship in surgery has not yet been established. Our objective was to study the effect of absolute hospital volume of anatomic lung resections and volume changes within hospitals on mortality, length of stay (LOS), readmission (RA), and post-discharge visits to the emergency room (ER visits), during a period of regionalization for lung surgery in Ontario, Canada, to test the theory of "practice makes perfect". Methods: For each anatomic lung resection in Ontario from 2004-2010 we determined the volume change within hospital as the difference in hospital operative volume between the year immediately prior to the date of surgery and year prior to that (Dvolume). We used generalized estimating equations and logistic regression, controlling for clustering within hospitals, to examine the effect of Dvolume, patient factors and year on operative mortality, RA and ER visits. Negative binomial regression was used for LOS. The absolute effect of volume, measured as the 12-month hospital volume prior to each procedure, on outcomes was also examined with the same models. Results: Higher hospital volumes were associated with significant improvements in mortality and length of stay, (for increases of 10 cases, mortality OR=0.98 [95%CI: 0.96-1.00] and LOS RR=0.98 [95% CI: 0.97-0.99]), but not for RA or ER visits. However, increases in within-hospital volume did not lead to changes in mortality (OR=1.00, 95% CI: 0.96-1.10), RA (OR=1.00, 95% CI: 0.99-1.00), or ER visits (OR=0.99, 95% CI: 0.98-1.00). Volume increases within hospitals did lead to small improvements in LOS (RR=0.996, 95% CI: 0.993-0.999). Conclusions: Increasing volumes within hospitals did not lead to improvements in mortality in our study, but did result in small improvements in LOS. The decrease in LOS was likely appropriate as it was not associated with changes in RA or ER visits. A volume-outcome relationship between absolute hospital volume and improved mortality was observed, but was not explained by increasing volumes within hospitals; thus practice did not make perfect for mortality.

scholarly journals Statistical models for analyzing count data: predictors of length of stay among HIV patients in Portugal using a multilevel model

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Ahmed Nabil Shaaban ◽  
Bárbara Peleteiro ◽  
Maria Rosario O. Martins
Keyword(s):  
Length Of Stay ◽  
Regression Model ◽  
Random Effects ◽  
Count Data ◽  
Negative Binomial ◽  
Negative Binomial Regression ◽  
Comprehensive Approach ◽  
Negative Binomial Regression Model ◽  
Hiv Patients ◽  
Binomial Regression

Abstract Background This study offers a comprehensive approach to precisely analyze the complexly distributed length of stay among HIV admissions in Portugal. Objective To provide an illustration of statistical techniques for analysing count data using longitudinal predictors of length of stay among HIV hospitalizations in Portugal. Method Registered discharges in the Portuguese National Health Service (NHS) facilities Between January 2009 and December 2017, a total of 26,505 classified under Major Diagnostic Category (MDC) created for patients with HIV infection, with HIV/AIDS as a main or secondary cause of admission, were used to predict length of stay among HIV hospitalizations in Portugal. Several strategies were applied to select the best count fit model that includes the Poisson regression model, zero-inflated Poisson, the negative binomial regression model, and zero-inflated negative binomial regression model. A random hospital effects term has been incorporated into the negative binomial model to examine the dependence between observations within the same hospital. A multivariable analysis has been performed to assess the effect of covariates on length of stay. Results The median length of stay in our study was 11 days (interquartile range: 6–22). Statistical comparisons among the count models revealed that the random-effects negative binomial models provided the best fit with observed data. Admissions among males or admissions associated with TB infection, pneumocystis, cytomegalovirus, candidiasis, toxoplasmosis, or mycobacterium disease exhibit a highly significant increase in length of stay. Perfect trends were observed in which a higher number of diagnoses or procedures lead to significantly higher length of stay. The random-effects term included in our model and refers to unexplained factors specific to each hospital revealed obvious differences in quality among the hospitals included in our study. Conclusions This study provides a comprehensive approach to address unique problems associated with the prediction of length of stay among HIV patients in Portugal.

scholarly journals Effect of Comorbidity Assessed by the Charlson Comorbidity Index on the Length of Stay, Costs and Mortality among Older Adults Hospitalised for Acute Stroke

2018 ◽  
Vol 15 (11) ◽  
pp. 2532 ◽  
Author(s):  
Richard Ofori-Asenso ◽  
Ella Zomer ◽  
Ken Chin ◽  
Si Si ◽  
Peter Markey ◽  
...  
Keyword(s):  
Older Adults ◽  
Length Of Stay ◽  
Acute Stroke ◽  
Charlson Comorbidity Index ◽  
Negative Binomial ◽  
Cox Regression ◽  
Negative Binomial Regression ◽  
Tertiary Hospital ◽  
Hospital Death ◽  
Comorbidity Index

The burden of comorbidity among stroke patients is high. The aim of this study was to examine the effect of comorbidity on the length of stay (LOS), costs, and mortality among older adults hospitalised for acute stroke. Among 776 older adults (mean age 80.1 ± 8.3 years; 46.7% female) hospitalised for acute stroke during July 2013 to December 2015 at a tertiary hospital in Melbourne, Australia, we collected data on LOS, costs, and discharge outcomes. Comorbidity was assessed via the Charlson Comorbidity Index (CCI), where a CCI score of 0–1 was considered low and a CCI ≥ 2 was high. Negative binomial regression and quantile regression were applied to examine the association between CCI and LOS and cost, respectively. Survival was evaluated with the Kaplan–Meier and Cox regression analyses. The median LOS was 1.1 days longer for patients with high CCI than for those with low CCI. In-hospital mortality rate was 18.2% (22.1% for high CCI versus 11.8% for low CCI, p < 0.0001). After controlling for confounders, high CCI was associated with longer LOS (incidence rate ratio [IRR]; 1.35, p < 0.0001) and increased likelihood of in-hospital death (hazard ratio [HR]; 1.91, p = 0.003). The adjusted median, 25th, and 75th percentile costs were AUD$2483 (26.1%), AUD$1446 (28.1%), and AUD$3140 (27.9%) higher for patients with high CCI than for those with low CCI. Among older adults hospitalised for acute stroke, higher global comorbidity (CCI ≥ 2) was associated adverse clinical outcomes. Measures to better manage comorbidities should be considered as part of wider strategies towards mitigating the social and economic impacts of stroke.

scholarly journals Measuring multimorbidity in hospitalised patients using linked hospital episode data: comparison of two measures

2019 ◽  
Vol 4 (1) ◽  
Author(s):  
Lynn Robertson ◽  
Dolapo Ayansina ◽  
Marjorie Johnston ◽  
Angharad Marks ◽  
Corri Black
Keyword(s):  
At Risk ◽  
Length Of Stay ◽  
Negative Binomial ◽  
Negative Binomial Regression ◽  
Resource Planning ◽  
Kappa Statistics ◽  
Data Systems ◽  
Hospital Episode ◽  
Hospitalised Patients ◽  
Poor Outcomes

IntroductionMultimorbidity is a complex and growing health challenge. There is no accepted “gold standard” multimorbidity measure for hospital resource planning, and few studies have compared measures in hospitalised patients. AimTo evaluate operationalisation of two multimorbidity measures in routine hospital episode data in NHS Grampian, Scotland. MethodsLinked hospital episode data (Scottish Morbidity Record (SMR)) for the years 2009-2016 were used. Adults admitted to hospital as a general/acute inpatient during 2014 were included. Conditions (ICD-10) were identified from general/acute (SMR01) and psychiatric (SMR04) admissions during the five years prior to first admission in 2014. Two count-based multimorbidity measures were used (Charlson Comorbidity Index and Tonelli et al.), and multimorbidity was defined as ≥2 conditions. Kappa statistics assessed agreement. The association between multimorbidity and length of stay, readmission and mortality was assessed using logistic and negative binomial regression as appropriate. ResultsIn 41,545 adults (median age 62 years, 52.6% female), multimorbidity prevalence was 15.1% (95% CI 14.8%, 15.5%) using Charlson and 27.4% (27.0%, 27.8%) using Tonelli – agreement 85.1% (Kappa 0.57). Multimorbidity prevalence, using both measures, increased with age. Multimorbidity was higher in males (16.5%) than females (13.9%) using the Charlson measure, but similar across genders when measured with Tonelli. After adjusting for covariates, multimorbidity remained associated with longer length of stay (Charlson IRR 1.1 (1.0, 1.2); Tonelli IRR 1.1 (1.0, 1.2)) and readmission (Charlson OR 2.1 (1.9, 2.2); Tonelli OR 2.1 (2.0, 2.2)). Multimorbidity had a stronger association with mortality when measured using Charlson (OR 2.7 (2.5, 2.9)), than using Tonelli (OR (1.8 (1.7, 2.0)). ConclusionsMultimorbidity measures operationalised in hospital episode data identified those at risk of poor outcomes and such operationalised tools will be useful for future multimorbidity research and use in secondary care data systems. Multimorbidity measures are not interchangeable, and the choice of measure should depend on the purpose. Hightlights Operationalisation of two count-based multimorbidity measures using linked electronic hospitalepisode data was evaluated (Charlson and Tonelli). First study to compare the Tonelli measure with another measure for investigating multimor-bidity in hospitalised patients. Multimorbidity prevalence differed depending on measure used, but both multimorbidity mea-sures identified those at risk of poor outcomes. Operationalised multimorbidity tools have uses for future multimorbidity research and use insecondary care data systems. Multimorbidity measures are not interchangeable, and choice of measure should depend onpurpose.

scholarly journals Does it matter what a hospital is “high volume” for? Specificity of hospital volume-outcome associations for surgical procedures: analysis of administrative data

BMJ ◽  
2004 ◽  
Vol 328 (7442) ◽  
pp. 737-740 ◽  
Author(s):  
David R Urbach ◽  
Nancy N Baxter
Keyword(s):  
Odds Ratio ◽  
Surgical Procedure ◽  
Hospital Volume ◽  
Colorectal Resection ◽  
Lung Resection ◽  
Secondary Data ◽  
High Volume ◽  
The Other ◽  
Inverse Association ◽  
Volume Outcome

AbstractObjective To determine whether the improved outcome of a surgical procedure in high volume hospitals is specific to the volume of the same procedure.Design and setting Analysis of secondary data in Ontario, Canada.Participants Patients having an oesophagectomy, colorectal resection for cancer, pancreaticoduodenectomy, major lung resection for cancer, or repair of an unruptured abdominal aortic aneurysm between 1994 and 1999.Main outcome measures Odds ratio for death within 30 days of surgery in relation to the hospital volume of the same surgical procedure and the hospital volume of the other four procedures. Estimates were adjusted for age, sex, and comorbidity and accounted for hospital level clustering.Results With the exception of colorectal resection, 30 day mortality seemed to be inversely related not only to the hospital volume of the same procedure but also to the hospital volume of most of the other procedures. In some cases the effect of the volume of a different procedure was stronger than the effect of the volume of the same procedure. For example, the association of mortality from pancreaticoduodenectomy with hospital volume of lung resection (odds ratio for death in hospitals with a high volume of lung resection compared with low volume 0.36, 95% confidence interval 0.23 to 0.57) was much stronger than the association of mortality from pancreaticoduodenectomy with hospital volume of pancreaticoduodenectomy (0.76, 0.44 to 1.32).Conclusion The inverse association between high volume of procedure and risk of operative death is not specific to the volume of the procedure being studied.

The Influence of Treatment Facility Volume on Survival of Non-Hodgkin Lymphoma: Determining the Volume-Outcome Relationship

Blood ◽  
2015 ◽  
Vol 126 (23) ◽  
pp. 266-266
Author(s):  
Ronald S. Go ◽  
Mohammed Al-Hamadani ◽  
Cynthia S Crowson ◽  
Nilay D Shah ◽  
Elizabeth B Habermann
Keyword(s):  
Hodgkin Lymphoma ◽  
Hospital Volume ◽  
Smoothing Splines ◽  
Cancer Data ◽  
Non Hodgkin Lymphoma ◽  
Outcome Relationship ◽  
The Us ◽  
Facility Type ◽  
Non Linear ◽  
Volume Outcome

Abstract Background: Non-Hodgkin lymphoma (NHL) is a relatively uncommon cancer with annual incidence of ~70,000 cases but with over 50 distinct subtypes. The goal of this study was to determine the extent to which the number of NHL patients treated annually in a facility (facility volume) affects overall survival (OS). This study used the National Cancer Data Base (NCDB), a nationwide oncology database covering 70% of the US cancer population, to address this question. Methods: We used the NCDB to identify patients with NHL diagnosed from 1998 to 2006. Year 2006 was used as a cut-off in order to allow a minimum of five years of follow-up for all patients. Only patients treated at facilities with continuous annual reporting to NCDB were included. We classified treatment facilities by quartiles based on facility volume (mean patients/year): Quartile 1 (Q1: 2-13), Quartile 2 (Q2: 14-20), Quartile 3 (Q3: 21-32) and Quartile 4 (Q4: ≥33). We used Pearson correlation methods to examine collinearity, unadjusted Kaplan-Meier methods to estimate OS rates, log rank test to compare survival distributions, and multivariable Cox proportional hazards model to examine the associations between hospital volume and OS adjusting for other covariates of interest. We also included random effects for hospital to more fully adjust for clustering of outcomes within hospitals. To examine non-linear effects of hospital volume, we utilized smoothing splines. Results: There were 278,985 NHL patients cared for at 1,151 facilities. The distribution of patients according to facility volume was Q1 (10.7%), Q2 (13.5%), Q3 (23.3%) and Q4 (52.5%) and according to facility type was academic (31.2%), comprehensive community (55.9%), community (10.6%) and other (2.3%) centers. The unadjusted median OS by facility volume was: Q1: 61.8 months, Q2: 65.9 months, Q3: 71.4 months and Q4: 83.6 months. After multivariable analysis adjusting for demographic (sex, age, race, ethnicity), socioeconomic (income, insurance type), geographic (area of residence), disease-specific (NHL subtype, stage) and facility-specific (type and location) factors, we show that facility volume remains an independent predictor of all-cause mortality. Compared to patients treated at Q4 facilities, patients treated at lower quartile facilities had a worse OS (Q3HR: 1.05 [95% CI, 1.04-1.06]; Q2HR: 1.08 [1.07-1.10]; Q1HR: 1.14 [1.11-1.17]). We adjusted for hospital as a random effect, performed sensitivity analyses removing primary payor and facility type (due to collinearity with age and facility volume, respectively), and adjusted for Charlson-Deyo co-morbidity score (available only for patients diagnosed after 2003) in secondary models and found similar results. Using smoothing splines, we found a significant non-linear effect of hospital volume on OS (P &lt;0.001). This is depicted in the Figure wherein the hazard ratio of 1.0 corresponded to the average predicted hazard, which occurred at a hospital volume of 59 patients per year. Conclusions: Patients who were treated for NHL at higher volume facilities had longer OS than those who were treated at facilities with a lower volume. This is the first study in the US using a national sample to show that a volume-outcome relationship exists in the medical management of cancer. Figure 1. Figure 1. Disclosures No relevant conflicts of interest to declare.

scholarly journals Influence of trends in hospital volume over time on patient outcomes for high-risk surgery

2019 ◽  
Author(s):  
Cécile Payet ◽  
Stéphanie Polazzi ◽  
Jean-Christophe Lifante ◽  
Eddy Cotte ◽  
Daniel Grinberg ◽  
...  
Keyword(s):  
High Risk ◽  
Patient Outcomes ◽  
Hospital Volume ◽  
Learning Effect ◽  
Hazard Ratios ◽  
Outcome Relationship ◽  
Volume Outcome ◽  
Procedure Volume ◽  
The Individual ◽  
Over Time

Abstract Background The more frequent a hospital performs a procedure, the better the outcome of the procedure; however, the mechanisms of this volume-outcome relationship have not been deeply elucidated to date. We aimed to determine whether patient outcomes improve in hospitals with a significantly increased volume of high-risk surgery over time and whether a learning effect existed at the individual hospital level. Methods We included all patients who underwent one of ten digestive, cardiovascular and orthopaedic procedures between 2010 and 2014 from the French nationwide hospitals database. For each procedure, we identified three groups of hospitals according to volume trend (increased, decreased, or no change). In-hospital mortality, reoperation, and unplanned hospital readmission within 30 days were compared between groups using Cox regressions, taking into account clustering of patients within hospitals and potential confounders. Learning effect was investigated by considering the interaction between hospital groups and procedure year. Results Over 5 years, 759,928 patients from 694 hospitals were analysed. Patients’ mortality in hospitals with procedure volume increase or decrease over time did not clearly differ from those in hospitals with unchanged volume across the studied procedures (e.g., Hazard Ratios [95%] of 1.04 [0.93-1.17] and 1.08 [0.97-1.21] respectively for colectomy). Furthermore, patient outcomes did not improve or deteriorate in hospitals with increased or decreased volume of procedures over time (e.g., 1.01 [0.95-1.08] and 0.99 [0.92-1.05] respectively for colectomy). Conclusions Trend in hospital volume over time does not appear to influence patient outcomes, which puts the relevance of the "practice-makes-perfect" dogma in question.

Abstract W MP97: Post-discharge Pneumonia And Urinary Tract Infection After Hospitalization For Ischemic And Hemorrhagic Strokes

Stroke ◽  
2015 ◽  
Vol 46 (suppl_1) ◽  
Author(s):  
Yogesh Moradiya ◽  
Santosh Murthy ◽  
Christa San Luis ◽  
Devanshi Dharaiya ◽  
Jaydeep Kachhela ◽  
...  
Keyword(s):  
Urinary Tract Infection ◽  
Ischemic Stroke ◽  
Urinary Tract ◽  
Tract Infection ◽  
Negative Binomial ◽  
Negative Binomial Regression ◽  
Infectious Complications ◽  
First Episode ◽  
Post Discharge ◽  
Binomial Regression

Background and purpose: Stroke related disability is a risk factor for infectious complications and the risk persists even after discharge from the hospital. Therefore, we studied the incidence and predictors of pneumonia and urinary tract infection (UTI) up to 1 year after hospitalization for ischemic stroke, spontaneous intracerebral hemorrhage (ICH) and non-traumatic subarachnoid hemorrhage (SAH). Methods: We analyzed all adult cases treated in acute care hospitals and emergency departments in California state between 2005 and 2011 using state inpatient and emergency databases of Healthcare Cost and Utilization Project. Patients with principle diagnosis of ischemic stroke (ICD-9 codes 433.x1, 434.x1, 436), ICH (431) and SAH (430) surviving up to discharge were followed up for development of first episode of pneumonia and UTI at 30-days, 90-days and 1-year intervals. Index cases were limited to 2005-2010 to ensure at least 1 year of follow up. Negative binomial regression was used to obtain predictors of post-discharge pneumonia or UTI. Results: Among 168,194 ischemic strokes, 26,502 ICH and 10,659 SAH cases, 5.7%, 7.3% and 4.9% developed pneumonia, and 8.6%, 11.5% and 8.8% developed UTI within 90-days after discharge respectively. The incidence rate of pneumonia during first 30 days was 38.8 per 100 person years which decreased to 14.8 per 100 person years between 31-90 days. The rate decreased further during 91-365 days to 7.3 per 100 person years. The rates of pneumonia among ICH and SAH and of UTI among different stroke types showed similar trend with higher risk during immediate post-discharge period. Factors independently associated with post-discharge infections were older age, female sex, non-white race, hypertension, diabetes, congestive heart failure, dementia, liver disease, anemia, atrial fibrillation, mechanical ventilation, gastrostomy and longer length of index admission. Conclusions: Risk of infectious complications is highest after ICH among different stroke types. The risk is highest during immediate post-discharge period but remains higher than general hospital population at least up to 1 year.

scholarly journals Volume Pledge is Not Associated with Better Short-Term Outcomes After Lung Cancer Resection

2020 ◽  
Vol 38 (30) ◽  
pp. 3518-3527
Author(s):  
Farhood Farjah ◽  
Maria V. Grau-Sepulveda ◽  
Henning Gaissert ◽  
Mark Block ◽  
Eric Grogan ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Length Of Stay ◽  
Failure To Rescue ◽  
Short Term ◽  
Major Morbidity ◽  
Cancer Resection ◽  
End Point ◽  
Outcome Relationship ◽  
Volume Outcome ◽  
Restricted Cubic Splines

PURPOSE We examined the relationship between short-term outcomes and hospitals and surgeons who met minimum volume thresholds for lung cancer resection based on definitions provided by the Volume Pledge. A secondary aim was to evaluate the volume-outcome relationship to determine alternative thresholds in the event the Volume Pledge was not associated with outcomes. PATIENTS AND METHODS We conducted a retrospective study (2015-2017) using the Society of Thoracic Surgeons General Thoracic Surgery Database. We used generalized estimating equations that accounted for confounding and clustering to compare outcomes across hospitals and surgeons who did and did not meet the Volume Pledge criteria: ≥ 40 patients per year for hospitals and ≥ 20 patients per year for surgeons. Our secondary aim was to model volume by using restricted cubic splines to determine the association between volume and short-term outcomes. RESULTS Among 32,183 patients, 465 surgeons, and 209 hospitals, 16,630 patients (52%) received care from both a hospital and surgeon meeting the Volume Pledge criteria. After adjustment, there was no relationship with operative mortality, complications, major morbidity, a major morbidity-mortality composite end point, or failure to rescue. The Volume Pledge group had a 0.5 day (95% CI, 0.2 to 0.7 day) shorter length of stay. Our secondary aim revealed a nonlinear relationship between hospital volume and complications in which intermediate-volume hospitals had the highest risk of complications. Surgeon volume was associated with major morbidity, a major morbidity-mortality composite end point, and length of stay in an inverse linear fashion. Only 8% of surgeons had volumes associated with better outcomes. CONCLUSION The Volume Pledge was not associated with better outcomes except for a marginally shorter length of stay. A re-examination of volume-outcome relationships for hospitals and surgeons yielded mixed results that did not reveal a practical alternative for volume-based quality improvement efforts.

Understanding Hospital Volume–Outcome Relationship in Severe Traumatic Brain Injury

Neurosurgery ◽  
2017 ◽  
Vol 80 (4) ◽  
pp. 534-542 ◽  
Author(s):  
Aziz S. Alali ◽  
David Gomez ◽  
Victoria McCredie ◽  
Todd G. Mainprize ◽  
Avery B. Nathens
Keyword(s):  
Traumatic Brain Injury ◽  
Brain Injury ◽  
Hospital Mortality ◽  
Hospital Volume ◽  
Major Complication ◽  
Major Complications ◽  
Outcome Relationship ◽  
Severe Tbi ◽  
Volume Outcome ◽  
Complications And Mortality

Abstract BACKGROUND: The hospital volume–outcome relationship in severe traumatic brain injury (TBI) population remains unclear. OBJECTIVE: To examine the relationship between volume of patients with severe TBI per hospital and in-hospital mortality, major complications, and mortality following a major complication (ie, failure to rescue). METHODS: In a multicenter cohort study, data on 9255 adults with severe TBI were derived from 111 hospitals participating in the American College of Surgeons Trauma Quality Improvement Program over 2009-2011. Hospitals were ranked into quartiles based on their volume of severe TBI during the study period. Random-intercept multilevel models were used to examine the association between hospital quartile of severe TBI volume and in-hospital mortality, major complications, and mortality following a major complication after adjusting for patient and hospital characteristics. In sensitivity analyses, we examined these associations after excluding transferred cases. RESULTS: Overall mortality was 37.2% (n = 3447). Two thousand ninety-eight patients (22.7%) suffered from 1 or more major complication. Among patients with major complications, 27.8% (n = 583) died. Higher-volume hospitals were associated with lower mortality; the adjusted odds ratio of death was 0.50 (95% confidence interval: 0.29-0.85) in the highest volume quartile compared to the lowest. There was no significant association between hospital-volume quartile and the odds of a major complication or the odds of death following a major complication. After excluding transferred cases, similar results were found. CONCLUSION: High-volume hospitals might be associated with lower in-hospital mortality following severe TBI. However, this mortality reduction was not associated with lower risk of major complications or death following a major complication.

Sign in / Sign up

Export Citation Format

Share Document