Jenna Wong

International variation in medication prescription rates among elderly patients with inflammatory bowel disease

BACKGROUND AND AIMS The elderly represent a growing demographic of patients with IBD. No study has previously described variations in care or medication prescriptions in senior patients with IBD. We compared prescription rates among elderly patients with IBD in four countries using health administrative data. METHODS Databases from the United States (US), United Kingdom (UK), Denmark and Canada were queried. Variation in prescription rates between countries was assessed in patients ≥65y with prevalent IBD who had ≥1 prescription for an IBD-related medication in a given quarter between 2004 and 2009. Patients were identified using previously-reported, validated algorithms. Country-specific rates were compared in each quarter using Fishers exact test. RESULTS In patients with Crohns disease, Canada and US had higher prescription rates for oral 5-ASA (P<0.0001 in all quarters) and infliximab (P<0.05 in 22/24 quarters), while the US had higher rates of thiopurine usage (P<0.05 in 23/24 quarters). Canada had greater rates of methotrexate prescriptions (P<0.05 in 21/24 quarters analyzed). In patients with ulcerative colitis (UC), rates of oral steroid usage was lowest in the US (P<0.05 in 22/24 quarters) and oral 5-ASA use was highest in the US and Canada (P<0.0001 in all quarters). Canada and Denmark used more rectal therapy than the US. Infliximab usage in UC was significantly higher in the US and Canada after 2006. CONCLUSIONS Significant variation in medication prescription rates exists among countries. Future research should assess whether these differences were associated with disparities in outcomes and health care costs.

Incidence, follow-up, and outcomes of incidental abdominal aortic aneurysms

BACKGROUND Incidental abdominal aortic aneurysms (AAAs) are identified during imaging for other reasons. Incidental AAAs are important findings because they require monitoring and surgical treatment, when indicated, to prevent rupture. The prevalence of incidental AAAs and their management has not been extensively studied. METHODS We electronically screened a 25% simple random sample of abdominal computed tomography (CT), ultrasound (US), and magnetic resonance imaging (MRI) studies conducted between 1996 and 2008 at one academic medical center. Screen-positive reports were manually reviewed to determine if they showed an incidental AAA. We reviewed the medical records of all in-patients to determine whether the incidental AAA was documented, a treatment plan was identified, and whether it was communicated to the patients family physician through the discharge summary. We used evidence-based recommended schedules to determine the adequacy of AAA monitoring for each person. RESULTS In 79,121 abdominal images, we identified 812 incidental AAAs (1.0% of all studies) or 364 incidental AAAs annually (95% confidence interval [CI], 349-379). Patients were elderly (mean age, 74 years), and AAAs were a mean diameter of 4.0 cm. For 174 inpatients, AAAs were noted in only 51 patients (29%) and only 25 (15%) were communicated to the family physician. Of 329 patients who were observed beyond their first recommended follow-up scan, only 51 (16%) were monitored appropriately throughout their entire follow-up; the median proportion of follow-up time with recommended monitoring was 56% (interquartile range, 32%-82%). Elective AAA repair was done in 98 patients (13%), the probability of which was significantly increased when AAA monitoring frequency was compliant with that recommended in practice guidelines. Six patients (0.8%) were admitted with aortic rupture, the probability of which was independent of AAA monitoring. CONCLUSION Incidental AAAs are common and appear to be poorly monitored. Our data suggested that improved monitoring of incidental AAAs was independently associated with elective AAA repair. Population-based analyses are required to determine the influence that monitoring has on incidental AAA rupture and patient mortality.

Comparing methods to calculate hospital-specific rates of early death or urgent readmission

Background: Hospital readmissions are important patient outcomes that can be accurately captured with routinely collected administrative data. Hospital-specific readmission rates have been reported as a quality-of-care indicator. However, the extent to which these measures vary with different calculation methods is uncertain. Methods: We identified all discharges from Ontario hospitals from 2005 to 2010 and determined whether patients died or were urgently readmitted within 30 days. For each hospital, we calculated 4 distinct observed-to-expected ratios, estimating the expected number of events using different adjustments for confounders (age and sex v. complete) and different units of analysis (all admissions v. single admission per patient). Results: We included 3 148 648 admissions to hospital for 1 802 704 patients in 162 hospitals. Ratios adjusted for age and sex alone had the greatest variation. Within hospitals, ranges of the 4 ratios averaged 31% of the overall estimate. Readmission ratios adjusted for age and sex showed the lowest correlation (Spearman correlation coefficient 0.48–0.68). Hospital rankings based on the different measures had an average range of 47.4 (standard deviation 32.2) out of 162. Interpretation: We found notable variation in rates of death or urgent readmission within 30 days based on the extent of adjustment for confounders and the unit of analysis. Slight changes in the methods used to calculate hospital-specific readmission rates influence their values and the consequent rankings of hospitals. Our results highlight the caution required when comparing hospital performance using rates of death or urgent readmission within 30 days.

Influence of neighborhood household income on early death or urgent hospital readmission

BACKGROUND The relationship of socioeconomic status (SES) with hospital readmissions is unclear. METHODS We used population-based administrative datasets to randomly select 40,827 adult Ontarians discharged from hospital to the community. Patient postal codes were linked to average neighborhood household-income quintiles. The association of this SES measure with 30-day death or urgent readmission was measured after controlling for outcome risk using a validated index, LACE+: length of stay (L), acuity of the admission (A), comorbidity of the patient (measured with the Charlson Comorbidity Index score (C), and emergency-department use (E). RESULTS Within 1 month of discharge, 2638 (6.5%) people died or were urgently readmitted. Lower neighborhood income was significantly associated with both an increased outcome risk (P < 0.0001) and LACE+ score. After adjusting for LACE+ score, neighborhood income was no longer associated with 30-day death or urgent readmission (P = 0.21). CONCLUSIONS After accounting for known risk factors, early death or readmission is not more common in people from lower-income neighborhoods. Further study is required to determine if SES is associated with adverse postdischarge outcomes in settings without publicly funded healthcare.

Addition of time-dependent covariates to a survival model significantly improved predictions for daily risk of hospital death

RATIONAL, AIMS AND OBJECTIVES The study aims to determine the extent to which the addition of post-admission information via time-dependent covariates improved the ability of a survival model to predict the daily risk of hospital death. METHOD Using administrative and laboratory data from adult inpatient hospitalizations at our institution between 1 April 2004 and 31 March 2009, we fit both a time-dependent and a time-fixed Cox model for hospital mortality on a randomly chosen 66% of hospitalizations. We compared the predictive performance of these models on the remaining hospitalizations. RESULTS All comparative measures clearly indicated that the addition of time-dependent covariates improved model discrimination and prominently improved model calibration. The time-dependent model had a significantly higher concordance probability (0.879 versus 0.811) and predicted significantly closer to the number of observed deaths within all risk deciles. Over the first 32 admission days, the integrated discrimination improvement (IDI) and net reclassification improvement (NRI) were consistently above zero (average IDI of +0.0200 and average NRI of 62.7% over the first 32 days). CONCLUSIONS The addition of time-dependent covariates significantly improved the ability of a survival model to predict a patients daily risk of hospital death. Researchers should consider adding time-dependent covariates when seeking to improve the performance of survival models.

The Procedural Index for Mortality Risk (PIMR): an index calculated using administrative data to quantify the independent influence of procedures on risk of hospital death

BackgroundSurgeries and other procedures can influence the risk of death in hospital. All published scales that predict post-operative death risk require clinical data and cannot be measured using administrative data alone. This study derived and internally validated an index that can be calculated using administrative data to quantify the independent risk of hospital death after a procedure.MethodsFor all patients admitted to a single academic centre between 2004 and 2009, we estimated the risk of all-cause death using the Kaiser Permanente Inpatient Risk Adjustment Methodology (KP-IRAM). We determined whether each patient underwent one of 503 commonly performed therapeutic procedures using Canadian Classification of Interventions codes and whether each procedure was emergent or elective. Multivariate logistic regression modeling was used to measure the association of each procedure-urgency combination with death in hospital independent of the KP-IRAM risk of death. The final model was modified into a scoring system to quantify the independent influence each procedure had on the risk of death in hospital.Results275 460 hospitalizations were included (137,730 derivation, 137,730 validation). In the derivation group, the median expected risk of death was 0.1% (IQR 0.01%-1.4%) with 4013 (2.9%) dying during the hospitalization. 56 distinct procedure-urgency combinations entered our final model resulting in a Procedural Index for Mortality Rating (PIMR) score values ranging from -7 to +11. In the validation group, the PIMR score significantly predicted the risk of death by itself (c-statistic 67.3%, 95% CI 66.6-68.0%) and when added to the KP-IRAM model (c-index improved significantly from 0.929 to 0.938).ConclusionsWe derived and internally validated an index that uses administrative data to quantify the independent association of a broad range of therapeutic procedures with risk of death in hospital. This scale will improve risk adjustment when administrative data are used for analyses.

A cross-sectional, population-based study of HIV physicians and outpatient health care use by people with HIV in Ontario

BackgroundPeople with HIV are living longer and their care has shifted towards the prevention and management of comorbidities. However, little is known about who is providing their care. Our objective was to characterize the provision of HIV care in Ontario by physician specialty.MethodsWe conducted a retrospective population-based observational study using linked administrative databases in Ontario, Canada, a single payer health care system. All Ontarians with HIV were identified using a validated case ascertainment algorithm. We examined office-based health care visits for this cohort between April 1, 2009 and March 31, 2012. Physician characteristics were compared between specialty groups. We stratified the frequency and distribution of physician care into three categories: (a) care by physician specialty (family physicians, internal medicine specialists, infectious disease specialists, and other specialists), (b) care based on physician caseload (low, medium or high categorized as ≤5, 6-49 or ≥50 HIV patients per physician), and (c) care that is related to HIV versus unrelated to HIV.ResultsFamily physicians were older, graduated earlier, were more often female, and were the only group practicing in rural settings. Unlike other specialists, most family physicians (76.8%) had low-volume caseloads. There were 406,411 outpatient visits made by individuals with HIV; one-third were for HIV care. Family physicians provided the majority of care (53.6% of all visits and 53.9% of HIV visits). Internal medicine specialists provided 4.9% of all visits and 9.6% of HIV visits. Infectious disease specialists provided 12.5% of all visits and 32.7% of HIV visits. Other specialties provided 29.0% of visits; most of these (33.0%) were to psychiatrists.ConclusionsThe distribution of visits to physicians caring for HIV patients reveals different patterns of health care delivery by specialty and HIV caseload. Further research should delineate how specialties share care for this population and how different patterns relate to quality of care.

Predicting post‐discharge death or readmission: deterioration of model performance in population having multiple admissions per patient

BACKGROUND To avoid biased estimates of standard errors in regression models, statisticians commonly limit the analytical dataset to one observation per patient. OBJECTIVE Measure and explain changes in model performance when a model predicting 30-day risk of death or urgent readmission (derived on a dataset having one hospitalization per patient) was applied to all hospitalizations for study patients. METHODS Using administrative data from Ontario, we identified all hospitalizations of 499,996 patients between 2004 and 2009. We calculated the expected risk for 30-day death or urgent readmission using a validated model. The observed-to-expected ratio was determined after categorizing patients into quintiles of rates for hospitalization, emergent hospitalizations, hospital day and total diagnostic risk score. RESULTS Study patients had a total of 858,410 hospitalizations. Compared with a dataset having one hospitalization per patient, model performance declined significantly when applied to all hospitalizations [c-statistic decreased from 0.768 to 0.730; the observed-to-expected ratio increased from 0.998 (95% confidence interval 0.977-0.999) to 1.305 (1.297-1.313)]. Model deterioration was most pronounced in patients with higher hospital utilization, with the observed-to-expected ratio increasing to 1.67 in the highest quintile of emergent hospitalization rates. CONCLUSIONS The accuracy of predicting 30-day death or urgent readmission decreased significantly when the unit of analysis changed from the patient to the hospitalization. Patients with heavy hospital utilization likely have characteristics, not adequately captured in the model, that increase the risk of death or urgent readmission after discharge from hospital. Adequately capturing the characteristics of such high-end hospital users may improve readmission models.

The influence of incidental abdominal aortic aneurysm monitoring on patient outcomes.

BACKGROUND Incidental abdominal aortic aneurysms (AAAs) are identified when the abdomen is imaged for other reasons. These are common, and many undergo incomplete radiological monitoring. The association between monitoring completeness and population-based outcomes has not been studied. METHODS A cohort of incidental AAAs (defined as previously unidentified aortic enlargement exceeding 3 cm found on an imaging study done for another reason) was linked to population-based data. Patients were followed to elective AAA repair, AAA rupture, death, or March 31, 2009. Monitoring completeness was gauged as the sequential number of months without a recommended abdominal scan. Its association with time to elective AAA repair and time to death was measured using a multivariable Cox regression model adjusting for other important covariates. RESULTS We identified 191 incidental AAAs between 1996 and 2004 (median diameter of 3.5 cm [range, 3.0-5.3 cm], median follow up of 4.4 years [range, 0.6-12.7 years]). During the study, patients spent a median of 19.4% of their time with incomplete AAA monitoring (interquartile range [IQR] 0.3%-44%); 56 patients (29.3%) had no follow-up imaging of their aneurysm. Nineteen patients (10.0%; 2.0% per year) underwent elective AAA repair, and 79 patients (37.7%; 7.6% per year) died. Independent of important covariates, people were significantly less likely to undergo elective repair (hazard ratio [HR], 0.03) and significantly more likely to die (HR, 2.99) if their AAA went without radiological monitoring for 1 year. CONCLUSIONS Incomplete incidental AAA radiological monitoring was significantly associated with a decreased risk of elective AAA repair and an increased risk of death. While uncontrolled confounding might explain part of these associations, clinicians should ensure that radiological monitoring of AAAs is complete in appropriate patients.

Predictors of the need for second intestinal resection in children with Crohnʼs disease: O-12.

Eric Benchimol, Médina Boualit, Jenna Wong, Jean-Frederic Colombel, Corinne Gower-Rousseau Division of Gastroenterology, Hepatology and Nutrition, Children’s Hospital of Eastern Ontario, University of Ottawa, Ottawa, ON, Canada, Institute for Clinical Evaluative Sciences, Ottawa, ON, Canada, Division of Gastroenterology, Hepatology and Nutrition, Hospital and University of Lille, Lille, Nord-Pas-de-Calais, France, Epidemiology Unit, EA2694, Hospital and University of Lille, Lille, Nord-Pas-de-Calais, France