David Wennberg

Case finding for patients at risk of readmission to hospital: development of algorithm to identify high risk patients

Abstract Objective To develop a method of identifying patients at high risk of readmission to hospital in the next 12 months for practical use by primary care trusts and general practices in the NHS in England. Data sources Data from hospital episode statistics showing all admissions in NHS trusts in England over five years, 1999-2000 to 2003-4; data from the 2001 census for England. Population All residents in England admitted to hospital in the previous four years with a subset of “reference” conditions for which improved management may help to prevent future admissions. Design Multivariate statistical analysis of routinely collected data to develop an algorithm to predict patients at highest risk of readmission in the next 12 months. The algorithm was developed by using a 10% sample of hospital episode statistics data for all of England for the period indicated. The coefficients for 21 most powerful (and statistically significant) variables were then applied against a second 10% test sample to validate the findings of the algorithm from the first sample. Results The key factors predicting subsequent admission included age, sex, ethnicity, number of previous admissions, and clinical condition. The algorithm produces a risk score (from 0 to 100) for each patient admitted with a reference condition. At a risk score threshold of 50, the algorithm identified 54.3% of patients admitted with a reference condition who would have an admission in the next 12 months; 34.7% of patients were “flagged” incorrectly (they would not have a subsequent admission). At risk score threshold levels of 70 and 80, the rate of incorrectly “flagged” patients dropped to 22.6% and 15.7%, but the algorithm found a lower percentage of patients who would be readmitted. The algorithm is made freely available to primary care trusts via a website. Conclusions A method of predicting individual patients at highest risk of readmission to hospital in the next 12 months has been developed, which has a reasonable level of sensitivity and specificity. Using various assumptions a “business case” has been modelled to demonstrate to primary care trusts and practices the potential costs and impact of an intervention using the algorithm to reduce hospital admissions.

The relationship between operator volume and outcomes after percutaneous coronary interventions in high volume hospitals in 1994–1996: The northern New England experience

OBJECTIVESnThe purpose of this study was to examine the relationship between annual operator volume and outcomes of percutaneous coronary interventions (PCIs) using contemporaneous data.nnnBACKGROUNDnThe 1997 American College of Cardiology (ACC)/American Heart Association task force based their recommendation that interventionists perform > or = 75 procedures per year to maintain competency in PCI on data collected largely in the early 1990s. The practice of interventional cardiology has since changed with the availability of new devices and drugs.nnnMETHODSnData were collected from 1994 through 1996 on 15,080 PCIs performed during 14,498 hospitalizations by 47 interventional cardiologists practicing at the five high volume (>600 procedures per hospital per year) hospitals in northern New England and one Massachusetts-based institution that support these procedures. Operators were categorized into terciles based on their annualized volume of procedures. Multivariate regression analysis was used to control for case-mix. In-hospital outcomes included death, emergency coronary artery bypass graft surgery (eCABG), non-emergency CABG (non-eCABG), myocardial infarction (MI), death and clinical success (> or = 1 attempted lesion dilated to < 50% residual stenosis and no death, CABG or MI).nnnRESULTSnAverage annual procedure rates varied across terciles from low = 68, middle = 115 and high = 209. After adjusting for case-mix, clinical success rates were comparable across terciles (low, middle and high terciles: 90.9%, 88.8% and 90.7%, Ptrend = 0.237), as were all the adverse outcomes including death (low-risk patients = 0.45%, 0.41%, 0.71%, Ptrend = 0.086; high-risk patients = 5.68%, 5.99%, 7.23%, Ptrend = 0.324), eCABG (1.74%, 2.05%, 1.75%, Ptrend = 0.733) and MI (2.57%, 1.90%, 1.86%, Ptrend = 0.065).nnnCONCLUSIONSnUsing current data, there is no significant relationship between operator volumes averaging > or = 68 per year and outcomes at high volume hospitals. Future efforts should be directed at determining the generalizability of these results.

A population health approach to reducing observational intensity bias in health risk adjustment: cross sectional analysis of insurance claims.

Objective To compare the performance of two new approaches to risk adjustment that are free of the influence of observational intensity with methods that depend on diagnoses listed in administrative databases. Setting Administrative data from the US Medicare program for services provided in 2007 among 306 US hospital referral regions. Design Cross sectional analysis. Participants 20% sample of fee for service Medicare beneficiaries residing in one of 306 hospital referral regions in the United States in 2007 (n=5 153 877). Main outcome measures The effect of health risk adjustment on age, sex, and race adjusted mortality and spending rates among hospital referral regions using four indices: the standard Centers for Medicare and Medicaid Services—Hierarchical Condition Categories (HCC) index used by the US Medicare program (calculated from diagnoses listed in Medicare’s administrative database); a visit corrected HCC index (to reduce the effects of observational intensity on frequency of diagnoses); a poverty index (based on US census); and a population health index (calculated using data on incidence of hip fractures and strokes, and responses from a population based annual survey of health from the Centers for Disease Control and Prevention). Results Estimated variation in age, sex, and race adjusted mortality rates across hospital referral regions was reduced using the indices based on population health, poverty, and visit corrected HCC, but increased using the standard HCC index. Most of the residual variation in age, sex, and race adjusted mortality was explained (in terms of weighted R2) by the population health index: R2=0.65. The other indices explained less: R2=0.20 for the visit corrected HCC index; 0.19 for the poverty index, and 0.02 for the standard HCC index. The residual variation in age, sex, race, and price adjusted spending per capita across the 306 hospital referral regions explained by the indices (in terms of weighted R2) were 0.50 for the standard HCC index, 0.21 for the population health index, 0.12 for the poverty index, and 0.07 for the visit corrected HCC index, implying that only a modest amount of the variation in spending can be explained by factors most closely related to mortality. Further, once the HCC index is visit corrected it accounts for almost none of the residual variation in age, sex, and race adjusted spending. Conclusion Health risk adjustment using either the poverty index or the population health index performed substantially better in terms of explaining actual mortality than the indices that relied on diagnoses from administrative databases; the population health index explained the majority of residual variation in age, sex, and race adjusted mortality. Owing to the influence of observational intensity on diagnoses from administrative databases, the standard HCC index over-adjusts for regional differences in spending. Research to improve health risk adjustment methods should focus on developing measures of risk that do not depend on observation influenced diagnoses recorded in administrative databases.

#112-S the effect of race on perioperative outcomes of eight high-risk cancer surgeries

PURPOSE: Racial disparities in access to healthcare have been well documented. However, less is known about differences in outcomes once access has been achieved. This study examined the relationship between race and operative mortality with eight high-risk cancer procedures. n nMETHODS: Using Medicare hospital claims data (1994–99), we used ICD-9 codes to identify patients undergoing colectomy, cystectomy, esophagectomy, gastrectomy, nephrectomy, pancreatic resection, pneumonectomy, or pulmonary lobectomy for cancer. Race (black/white) was defined from the Medicare enrollment file. Our primary outcome measure was operative mortality (death within 30 days or before discharge). Logistic regression was used to adjust mortality differences for patient characteristics (age, sex, socioeconomic status, acuity, and comorbidities) and provider factors (procedure volume). n nRESULTS: Trends toward higher mortality rates in blacks were noted for 7 of the 8 procedures (except gastrectomy). The largest mortality difference was observed among patients undergoing esophagectomy (blacks 21.9% vs. whites 15.2%). Blacks tended to be younger than whites and were more likely to be admitted emergently. Blacks were more likely than whites to undergo their procedures at low volume hospitals. After adjusting for these patient and provider variables, blacks remained significantly more likely to die after colectomy, esophagectomy, and nephrectomy (p < 0.001). n nCONCLUSION: For some high-risk cancer procedures, blacks have higher mortality rates than whites. Racial differences in operative mortality are not explained by measured patient or provider characteristics.

The relationship between operator volume and outcomes after percutaneous coronary interventions in high volume hospitals in 1994–199611A list of members of the Northern New England Cardiovascular Disease Study Group appears in Appendix B.

OBJECTIVESnThe purpose of this study was to examine the relationship between annual operator volume and outcomes of percutaneous coronary interventions (PCIs) using contemporaneous data.nnnBACKGROUNDnThe 1997 American College of Cardiology (ACC)/American Heart Association task force based their recommendation that interventionists perform > or = 75 procedures per year to maintain competency in PCI on data collected largely in the early 1990s. The practice of interventional cardiology has since changed with the availability of new devices and drugs.nnnMETHODSnData were collected from 1994 through 1996 on 15,080 PCIs performed during 14,498 hospitalizations by 47 interventional cardiologists practicing at the five high volume (>600 procedures per hospital per year) hospitals in northern New England and one Massachusetts-based institution that support these procedures. Operators were categorized into terciles based on their annualized volume of procedures. Multivariate regression analysis was used to control for case-mix. In-hospital outcomes included death, emergency coronary artery bypass graft surgery (eCABG), non-emergency CABG (non-eCABG), myocardial infarction (MI), death and clinical success (> or = 1 attempted lesion dilated to < 50% residual stenosis and no death, CABG or MI).nnnRESULTSnAverage annual procedure rates varied across terciles from low = 68, middle = 115 and high = 209. After adjusting for case-mix, clinical success rates were comparable across terciles (low, middle and high terciles: 90.9%, 88.8% and 90.7%, Ptrend = 0.237), as were all the adverse outcomes including death (low-risk patients = 0.45%, 0.41%, 0.71%, Ptrend = 0.086; high-risk patients = 5.68%, 5.99%, 7.23%, Ptrend = 0.324), eCABG (1.74%, 2.05%, 1.75%, Ptrend = 0.733) and MI (2.57%, 1.90%, 1.86%, Ptrend = 0.065).nnnCONCLUSIONSnUsing current data, there is no significant relationship between operator volumes averaging > or = 68 per year and outcomes at high volume hospitals. Future efforts should be directed at determining the generalizability of these results.