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ABSTRACT: 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. Journal of Hospital Medicine 2013. © 2013 Society of Hospital Medicine.
Journal of Hospital Medicine 04/2013; · 1.40 Impact Factor
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JAMA internal medicine. 03/2013;
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Ian G Stiell,
Catherine M Clement,
Robert J Brison,
Brian H Rowe,
Bjug Borgundvaag,
Shawn D Aaron,
Eddy Lang,
Lisa A Calder,
Jeffrey J Perry, Alan J Forster,
George A Wells
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ABSTRACT: There are no validated guidelines to guide physicians with difficult disposition decisions for emergency department (ED) patients with heart failure (HF). The authors sought to develop a risk scoring system to identify HF patients at high risk for serious adverse events (SAEs).
This was a prospective cohort study at six large Canadian EDS that enrolled adult patients who presented with acute decompensated HF. Each patient was assessed for standardized clinical and laboratory variables as well as for SAEs defined as death, intubation, admission to a monitored unit, or relapse requiring admission. Adjusted odds ratios for predictors of SAEs were calculated by stepwise logistic regression.
In 559 visits, 38.1% resulted in patient admission. Of 65 (11.6%) SAE cases, 31 (47.7%) occurred in patients not initially admitted. The multivariate model and resultant Ottawa Heart Failure Risk Scale consists of 10 elements, and the risk of SAEs varied from 2.8% to 89.0%, with good calibration between observed and expected probabilities. Internal validation showed the risk scores to be very accurate across 1,000 replications using the bootstrap method. A threshold of 1, 2, or 3 total scores for admission would be associated with sensitivities of 95.2, 80.6, or 64.5%, respectively, all better than current practice.
Many HF patients are discharged home from the ED and then suffer SAEs or death. The authors have developed an accurate risk scoring system that could ultimately be used to stratify the risk of poor outcomes and to enable rational and safe disposition decisions.
Academic Emergency Medicine 01/2013; 20(1):17-26. · 1.86 Impact Factor
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ABSTRACT: OBJECTIVES: Hospitals have strong incentives to decrease readmission rates. Not all hospital readmissions are potentially avoidable. Therefore, only a component of all hospital readmissions can be influenced by interventions designed to decrease them. In this study, we determined how effective interventions must be to attain specific reductions in hospital readmission rates. STUDY DESIGN AND SETTING: A conceptual model of all readmissions and potentially avoidable readmissions was used to derive a mathematical relationship between the relative reduction in the total number of readmissions, the relative reduction in potentially avoidable readmissions, and the proportion of readmissions that are potentially avoidable. RESULTS: When 22% of readmissions were potentially avoidable, achieving a 20% reduction in the total number of readmissions required a 91% reduction in potentially avoidable readmissions; decreasing potentially avoidable readmissions by 20% reduced total readmissions by 4.4%. CONCLUSION: These results highlight that relative reductions in the total number of readmissions are notably lower than that for potentially avoidable readmissions. This separation in relative reduction of all and potentially avoidable readmissions increases as the proportion of readmissions deemed potentially avoidable decreases. These results have important implications for health care planners and researchers.
Journal of clinical epidemiology 12/2012; · 2.96 Impact Factor
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ABSTRACT: 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.
Journal of Evaluation in Clinical Practice 11/2012; · 1.23 Impact Factor
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ABSTRACT: 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.
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).
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.
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.
Canadian Medical Association Journal 10/2012; 184(15):E810-7. · 8.22 Impact Factor
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ABSTRACT: Most of the urgent readmissions are unavoidable. This study developed a method that used observed urgent readmission rates to compare the latent avoidable readmission rates between the two hospitals.
To compare two hospitals, we identified all proportions of urgent readmissions deemed avoidable at each hospital making their avoidable readmission rates significantly different. We then calculated the probability that any of these conditions occurred. We applied this method to 25 randomly selected Ontario acute-care hospitals in 2008.
The hospitals had a median 30-day urgent readmission rate of 10.8% (interquartile [IQR] 9.7-12.8%). The median P-value of the 300 hospital-hospital comparisons for 30-day urgent readmission rate was 0.05 (interquartile range [IQR] 0.0005-0.31). In contrast, the median probability that hospitals with the lower urgent 30-day readmission rate outperformed their comparator hospital with respect to avoidable readmissions was only 0.161 (IQR 0.079-0.274).
Urgent readmission rates can be used to estimate the probability that avoidable readmission rates differ significantly between the two hospitals. The probability that avoidable readmission rates differ significantly between hospitals is small even when significant differences in urgent 30-day readmission rates exist. Our results show that 30-day urgent readmission rates should be used very cautiously to compare hospital quality of care.
Journal of clinical epidemiology 07/2012; 65(10):1124-30. · 2.96 Impact Factor
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ABSTRACT: Practice guidelines suggest that patients with obstructive sleep apnea (OSA) should be monitored postoperatively to reduce adverse events. This study evaluated outcomes following ambulatory surgery in patients who had previously undergone polysomnography (PSG), and compared unplanned admissions in patients diagnosed with OSA with those in patients without OSA.
A historical cohort study (July 2003 to March 2009) was conducted using administrative data and supplemented by selective chart review. Patients undergoing ambulatory surgery at the Ottawa Hospital who had a previously documented PSG were identified. The PSG reports were reviewed, and the presence and severity of OSA was determined. Unplanned admissions to hospital within seven days of surgery were identified using administrative data. Using a nested case-control design, three charts were randomly selected for each patient admitted for a focussed health records review. Event rates in patients with OSA and treated with continuous airway pressure were compared with event rates in patients without OSA. An exploratory multivariable analysis was conducted to identify predictors of admission.
There were 77,809 ambulatory surgical procedures in the period studied. A PSG test could be analyzed in 1,547 patients, and OSA was diagnosed in 674 (44%) of those analyzed. The rate of unplanned admission was 7.0% (95% confidence interval [CI] 5.1 to 8.9) in OSA patients compared with 5.6% (95% CI 4.1 to 7.1) in patients without OSA (odds ratio 1.26; 95% CI 0.83 to 1.91; P = 0.246). Median [interquartile range; IQR] hospital length of stay was 7 hr [IQR 5, 8] with OSA and 6 hr [IQR 5, 8] without OSA (P = 0.058). Severity of OSA was not associated with unplanned admission.
We did not identify a clinically important increased rate of unplanned admission associated with a prior diagnosis of OSA.
Canadian Anaesthetists? Society Journal 07/2012; 59(9):842-51. · 2.31 Impact Factor
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ABSTRACT: There are sparse data on how emergency health professionals make the important decision of emergency department (ED) patient admission or discharge, also known as the disposition decision. This study seeks to create a process map, a visual step-by-step diagram, and highlight error-prone areas for disposition decisions for high-acuity or nonambulatory ED patients.
We conducted 6 focus groups at an academic tertiary care ED: residents, social workers and registered nurses, registered nurses only, attending physicians, patient safety committee members, and consensus group from the 5 preceding groups. We asked participants to create a disposition decision process map and identify error-prone areas. We audiotaped, transcribed, and analyzed the sessions for themes, using qualitative techniques.
Forty-two stakeholders with clinical experience from 1 to 30 years participated. We found 9 dominant themes (ordered according to prevalence): triage, ED location of patient assessment, monitoring, diagnosis, departmental busyness, clinical gestalt, response to treatment, social work involvement, and patient and family communication. Groups identified overarching themes such as risk stratification and administrative policy. One group included dynamic elements such as interactions with consultants and handover. Participants described the following contributors to disposition error: triage, diagnostic error, communication error, ED location of patient assessment, and ED crowding.
Participants endorsed triage, diagnostic error, communication error, ED location of patient assessment, and ED crowding as the most important contributors to ED disposition decisionmaking errors. Understanding these factors in clinical decisionmaking is fundamental to improving future ED patient safety.
Annals of emergency medicine 06/2012; 60(5):567-576.e4. · 4.23 Impact Factor
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ABSTRACT: INTRODUCTIONAdverse events (AEs) are poor outcomes caused by medical care. They occur in 20% of medical patients following hospital discharge.
We designed an interactive voice response system (IVRS) with the intent of identifying patients who might be experiencing
an AE following discharge or were at risk of developing one.
OBJECTIVESWe determined the proportion of post-discharge patients requiring an intervention after identifying potential problems using
the IVRS, the relationship between IVRS responses and AE occurrence, and patients’ opinions of the IVRS call.
METHODSWe studied patients discharged from the general medical service of an academic hospital. The IVRS called patients 2 days post-discharge
and asked three questions to determine the need for nurse follow-up. We contacted patients 30days later to elicit AE status
and perceptions of the IVRS.
RESULTSOur cohort consisted of 270 elderly patients [median 64years (IQR 50-76)] with multiple co-morbidities. Responses to the
IVRS identified 57 patients (21%, 95% CI 17%-27%) for follow-up. When contacted by a nurse, 25 patients (9%, 95% CI 6%-13%)
actually required an intervention. At 30-day follow-up, AEs occurred in 33 patients (12%, 95% CI 8%-17%). Only three AEs (9%)
were identified by the IVRS; the remainder occurred before or after the IVRS call. Patients remembering the IVRS call found
it easy to use (97%), and a minority would prefer a person to call (8%).
CONCLUSIONAn IVRS-based method of monitoring was acceptable to patients and identified a significant proportion requiring changes in
management. However, the method identified only a minority of AEs. To have a significant improvement in care, this method
will need to be combined with other interventions.
Journal of General Internal Medicine 04/2012; 24(4):520-525. · 2.83 Impact Factor
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ABSTRACT: 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 patient's daily risk of hospital death. Researchers should consider adding time-dependent covariates when seeking to improve the performance of survival models.
Journal of Evaluation in Clinical Practice 03/2012; · 1.23 Impact Factor
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ABSTRACT: Adverse drug events (ADEs), defined as adverse patient outcomes caused by medications, are common and difficult to detect. Electronic detection of ADEs is a promising method to identify ADEs. We performed this systematic review to characterize established electronic detection systems and their accuracy.
We identified studies evaluating electronic ADE detection from the MEDLINE and EMBASE databases. We included studies if they contained original data and involved detection of electronic triggers using information systems. We abstracted data regarding rule characteristics including type, accuracy, and rationale.
Forty-eight studies met our inclusion criteria. Twenty-four (50%) studies reported rule accuracy but only 9 (18.8%) utilized a proper gold standard (chart review in all patients). Rule accuracy was variable and often poor (range of sensitivity: 40%-94%; specificity: 1.4%-89.8%; positive predictive value: 0.9%-64%). 5 (10.4%) studies derived or used detection rules that were defined by clinical need or the underlying ADE prevalence. Detection rules in 8 (16.7%) studies detected specific types of ADEs.
Several factors led to inaccurate ADE detection algorithms, including immature underlying information systems, non-standard event definitions, and variable methods for detection rule validation. Few ADE detection algorithms considered clinical priorities. To enhance the utility of electronic detection systems, there is a need to systematically address these factors.
Journal of the American Medical Informatics Association 01/2012; 19(1):31-8. · 3.61 Impact Factor
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ABSTRACT: Adverse events are poor patient outcomes caused by medical care. Their identification requires the peer-review of poor outcomes, which may be unreliable. Combining physician ratings might improve the accuracy of adverse event classification.
To evaluate the variation in peer-reviewer ratings of adverse outcomes; determine the impact of this variation on estimates of reviewer accuracy; and determine the number of reviewers who judge an adverse event occurred that is required to ensure that the true probability of an adverse event exceeded 50%, 75% or 95%.
Thirty physicians rated 319 case reports giving details of poor patient outcomes following hospital discharge. They rated whether medical management caused the outcome using a six-point ordinal scale. We conducted latent class analyses to estimate the prevalence of adverse events as well as the sensitivity and specificity of each reviewer. We used this model and Bayesian calculations to determine the probability that an adverse event truly occurred to each patient as function of their number of positive ratings.
The overall median score on the 6-point ordinal scale was 3 (IQR 2,4) but the individual rater median score ranged from a minimum of 1 (in four reviewers) to a maximum median score of 5. The overall percentage of cases rated as an adverse event was 39.7% (3798/9570). The median kappa for all pair-wise combinations of the 30 reviewers was 0.26 (IQR 0.16, 0.42; Min = -0.07, Max = 0.62). Reviewer sensitivity and specificity for adverse event classification ranged from 0.06 to 0.93 and 0.50 to 0.98, respectively. The estimated prevalence of adverse events using a latent class model with a common sensitivity and specificity for all reviewers (0.64 and 0.83 respectively) was 47.6%. For patients to have a 95% chance of truly having an adverse event, at least 3 of 3 reviewers are required to deem the outcome an adverse event.
Adverse event classification is unreliable. To be certain that a case truly represents an adverse event, there needs to be agreement among multiple reviewers.
PLoS ONE 01/2012; 7(7):e41239. · 4.09 Impact Factor
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ABSTRACT: The effect of hospital-acquired infection with Clostridium difficile on length of stay in hospital is not yet fully understood. We determined the independent impact of hospital-acquired infection with C. difficile on length of stay in hospital.
We conducted a retrospective observational cohort study of admissions to hospital between July 1, 2002, and Mar. 31, 2009, at a single academic hospital. We measured the association between infection with hospital-acquired C. difficile and time to discharge from hospital using Kaplan-Meier methods and a Cox multivariable proportional hazards regression model. We controlled for baseline risk of death and accounted for C. difficile as a time-varying effect.
Hospital-acquired infection with C. difficile was identified in 1393 of 136,877 admissions to hospital (overall risk 1.02%, 95% confidence interval [CI] 0.97%-1.06%). The crude median length of stay in hospital was greater for patients with hospital-acquired C. difficile (34 d) than for those without C. difficile (8 d). Survival analysis showed that hospital-acquired infection with C. difficile increased the median length of stay in hospital by six days. In adjusted analyses, hospital-acquired C. difficile was significantly associated with time to discharge, modified by baseline risk of death and time to acquisition of C. difficile. The hazard ratio for discharge by day 7 among patients with hospital-acquired C. difficile was 0.55 (95% CI 0.39-0.70) for patients in the lowest decile of baseline risk of death and 0.45 (95% CI 0.32-0.58) for those in the highest decile; for discharge by day 28, the corresponding hazard ratios were 0.74 (95% CI 0.60-0.87) and 0.61 (95% CI 0.53-0.68).
Hospital-acquired infection with C. difficile significantly prolonged length of stay in hospital independent of baseline risk of death.
Canadian Medical Association Journal 12/2011; 184(1):37-42. · 8.22 Impact Factor
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ABSTRACT: Rationale and objectives Urgent readmission to hospital is commonly used to measure hospital quality of care. Hospitals that measure the proportion of urgent readmissions judged avoidable need to know previously published rates for comparison. In this study, we generated a literature-based estimate for the proportion of 30-day urgent readmissions deemed avoidable for hospitals to use to gauge their performance in avoidable readmissions. Methods We searched the Medline and Embase databases to identify published studies that reported the proportion of 30-day urgent readmissions deemed avoidable. We then modelled the overall proportion of 30-day urgent readmissions deemed avoidable. Results We included 16 studies that used a wide variety of patients and a diverse range of methods to classify readmissions as avoidable. Studies reported a broad range for the proportion of urgent 30-day readmissions deemed avoidable. Overall, 848 of 3669 readmissions (23.1%, 95% confidence interval, 21.7-24.5) of 30-day urgent readmissions were classified as avoidable. This proportion varied significantly based on hospital teaching status and number of reviewers for each case [teaching hospitals: with one reviewer, 9.3% (4.2-19.3); with >1 reviewer, 21.6% (13.2-33.3); non-teaching hospital: with one reviewer, 32.2% (11.4-63.9); with >1 reviewer, 39.9% (37.6-42.2)]. Significant heterogeneity remained between studies even after clustering studies by these covariates. Conclusions Less than one in four readmissions were deemed avoidable. Health system planners need to use caution in interpreting all cause readmission statistics as they are only partially influenced by quality of care.
Journal of Evaluation in Clinical Practice 11/2011; · 1.23 Impact Factor
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ABSTRACT: Dual-process psychological theories argue that clinical decision making is achieved through a combination of experiential (fast and intuitive) and rational (slower and systematic) cognitive processes.
To determine whether emergency physicians perceived their clinical decisions in general to be more experiential or rational and how this compared with other physicians.
A validated psychometric tool, the Rational Experiential Inventory (REI-40), was sent through postal mail to all emergency physicians registered with the College of Physicians and Surgeons of Ontario, according to their website in November 2009. Forty statements were ranked on a Likert scale from 1 (Definitely False) to 5 (Definitely True). An initial survey was sent out, followed by reminder cards and a second survey to non-respondents. Analysis included descriptive statistics, Student t tests, analysis of variance and comparison of mean scores with those of cardiologists from New Zealand.
The response rate in this study was 46.9% (434/925). The respondents' median age was 41-50 years; they were mostly men (72.6%) and most had more than 10 years of clinical experience (66.8%). The mean REI-40 rational scores were higher than the experiential scores (3.93/5 (SD 0.35) vs 3.33/5 (SD 0.49), p<0.0001), similar to the mean scores of cardiologists from New Zealand (mean rational 3.93/5, mean experiential 3.05/5). The mean experiential scores were significantly higher for female respondents than for male respondents (3.40/5 (SD 0.49) vs 3.30/5 (SD 0.48), p=0.003).
Overall, emergency physicians favoured rational decision making rather than experiential decision making; however, female emergency physicians had higher experiential scores than male emergency physicians. This has important implications for future knowledge translation and decision support efforts among emergency physicians.
Emergency Medicine Journal 11/2011; 29(10):811-6. · 1.44 Impact Factor
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ABSTRACT: The "July phenomenon" refers to a purported worsening of outcomes in teaching-hospital patients with the arrival of new, inexperienced house-staff. Previous quantitative studies of new house-staff and increased mortality have been limited primarily by a focused patient population and the use of limited data to adjust for severity of patient illness.
We included all medicine, surgical, and obstetrical patients admitted to a teaching hospital in Ontario, Canada between April 15, 2004 and December 31, 2008. We calculated the ratio of observed to expected weekly number of deaths in hospital. The expected number of deaths was calculated using a validated, discriminative, and well-calibrated multivariate survival model. Collective house-staff experience was modeled from a minimum on July 1st to a maximum on June 30th using five distinct patterns.
We studied 259,748 encounters that included 164,318 people. The mortality rate was 3.0%. The ratio of observed to expected number of weekly deaths was not associated with collective house-staff experience, irrespective of the pattern in which it was modeled. The lack of association between risk of death in hospital and house-staff experience did not vary by admission type (urgent vs elective) or specialty (medicine vs surgery).
At our hospital, we found no association between the arrival of new house-staff and the adjusted risk of death in hospital. These data, along with the results of the vast majority of previous studies in this field, make the existence of the "July Phenomenon" for inpatient mortality extremely unlikely.
Journal of Hospital Medicine 09/2011; 6(7):389-94. · 1.40 Impact Factor
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ABSTRACT: Urgent, unplanned hospital readmissions are increasingly being used to gauge the quality of care. We reviewed urgent readmissions to determine which were potentially avoidable and compared rates of all-cause and avoidable readmissions.
In a multicentre, prospective cohort study, we reviewed all urgent readmissions that occurred within six months among patients discharged to the community from 11 teaching and community hospitals between October 2002 and July 2006. Summaries of the readmissions were reviewed by at least four practising physicians using standardized methods to judge whether the readmission was an adverse event (poor clinical outcome due to medical care) and whether the adverse event could have been avoided. We used a latent class model to determine whether the probability that each readmission was truly avoidable exceeded 50%.
Of the 4812 patients included in the study, 649 (13.5%, 95% confidence interval [CI] 12.5%-14.5%) had an urgent readmission within six months after discharge. We considered 104 of them (16.0% of those readmitted, 95% CI 13.3%-19.1%; 2.2% of those discharged, 95% CI 1.8%-2.6%) to have had a potentially avoidable readmission. The proportion of patients who had an urgent readmission varied significantly by hospital (range 7.5%-22.5%; χ(2) = 92.9, p < 0.001); the proportion of readmissions deemed avoidable did not show significant variation by hospital (range 1.2%-3.7%; χ(2) = 12.5, p < 0.25). We found no association between the proportion of patients who had an urgent readmission and the proportion of patients who had an avoidable readmission (Pearson correlation 0.294; p = 0.38). In addition, we found no association between hospital rankings by proportion of patients readmitted and rankings by proportion of patients with an avoidable readmission (Spearman correlation coefficient 0.28, p = 0.41).
Urgent readmissions deemed potentially avoidable were relatively uncommon, comprising less than 20% of all urgent readmissions following hospital discharge. Hospital-specific proportions of patients who were readmitted were not related to proportions with a potentially avoidable readmission.
Canadian Medical Association Journal 08/2011; 183(14):E1067-72. · 8.22 Impact Factor
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ABSTRACT: 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.
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.
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.
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.
Journal of vascular surgery: official publication, the Society for Vascular Surgery [and] International Society for Cardiovascular Surgery, North American Chapter 07/2011; 54(5):1290-1297.e2. · 3.52 Impact Factor
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ABSTRACT: Clinicians informally assess changes in patients' status over time to prognosticate their outcomes. The incorporation of trends in patient status into regression models could improve their ability to predict outcomes. In this study, we used a unique approach to measure trends in patient hospital death risk and determined whether the incorporation of these trend measures into a survival model improved the accuracy of its risk predictions.
We included all adult inpatient hospitalizations between 1 April 2004 and 31 March 2009 at our institution. We used the daily mortality risk scores from an existing time-dependent survival model to create five trend indicators: absolute and relative percent change in the risk score from the previous day; absolute and relative percent change in the risk score from the start of the trend; and number of days with a trend in the risk score. In the derivation set, we determined which trend indicators were associated with time to death in hospital, independent of the existing covariates. In the validation set, we compared the predictive performance of the existing model with and without the trend indicators.
Three trend indicators were independently associated with time to hospital mortality: the absolute change in the risk score from the previous day; the absolute change in the risk score from the start of the trend; and the number of consecutive days with a trend in the risk score. However, adding these trend indicators to the existing model resulted in only small improvements in model discrimination and calibration.
We produced several indicators of trend in patient risk that were significantly associated with time to hospital death independent of the model used to create them. In other survival models, our approach of incorporating risk trends could be explored to improve their performance without the collection of additional data.
BMC Health Services Research 07/2011; 11:171. · 1.66 Impact Factor
