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BACKGROUND Failure-to-rescue (FTR), originally developed to study quality of care in surgery, measures an institution's ability to prevent death after a patient becomes complicated. OBJECTIVES Develop an FTR metric modified to analyze acute myocardial infarction (AMI) outcomes. RESEARCH DESIGN Split-sample design: a random 20% of hospitals to develop FTR definitions, a second 20% to validate test characteristics, and an out-of-sample 60% to validate results. SUBJECTS Older Medicare beneficiaries admitted to short-term acute-care hospitals for AMI between 2009 and 2011. MEASURES Thirty-day mortality and FTR rates, and in-hospital complication rates. RESULTS The 60% out-of-sample validation included 234,277 patients across 1142 hospitals that admitted at least 50 patients over 2.5 years. In total, 72.1% of patients were defined as Medically Complicated (complex on admission or subsequently developed a complication or died without a recorded complication) of whom 19.3% died. Spearman r between hospital risk-adjusted 30-day mortality and FTR was 0.89 (P

Although the relationship between surgical volume and mortality is well established, the mechanisms underlying these associations remain uncertain. We sought to determine whether increased mortality at low-volume centers was due to higher complication rates or less success in rescuing patients from complications.Using 2005 to 2007 Medicare data, we identified patients undergoing 3 high-risk cancer operations: gastrectomy, pancreatectomy, and esophagectomy. We first ranked hospitals according to their procedural volume for these operations and divided them into 5 equal groups (quintiles) based on procedure volume cutoffs that most closely resulted in an equal distribution of patients through the quintiles. We then compared the incidence of major complications and "failure to rescue" (ie, case fatality among patients with complications) across hospital quintiles. We performed this analysis for all operations combined and for each operation individually.With all 3 operations combined, failure to rescue had a much stronger relationship to hospital volume than postoperative complications. Very low-volume (lowest quintile) hospitals had only slightly higher complications rates (42.7% vs. 38.9%; odds ratio 1.17, 95% confidence interval, 1.02-1.33), but markedly higher failure-to-rescue rates (30.3% vs. 13.1%; odds ratio 2.89, 95% confidence interval, 2.40-3.48) compared with very high-volume hospitals (highest quintile). These relationships also held true for individual operations. For example, patients undergoing pancreatectomy at very low-volume hospitals were 1.7 times more likely to have a major complication than those at very high-volume hospitals (38.3% vs. 27.7%, P0.05), but 3.2 times more likely to die once those complications had occurred (26.0% vs. 9.9%, P0.05).Differences in mortality between high and low-volume hospitals are not associated with large differences in complication rates. Instead, these differences seem to be associated with the ability of a hospital to effectively rescue patients from complications. Strategies focusing on the timely recognition and management of complications once they occur may be essential to improving outcomes at low-volume hospitals.

To examine the association between registered nurse (RN) staffing and patient outcomes in acute care hospitals.Twenty-eight studies reported adjusted odds ratios of patient outcomes in categories of RN-to-patient ratio, and met inclusion criteria. Information was abstracted using a standardized protocol.Random effects models assessed heterogeneity and pooled data from individual studies. Increased RN staffing was associated with lower hospital related mortality in intensive care units (ICUs) [odds ratios (OR), 0.91; 95% confidence interval (CI), 0.86-0.96], in surgical (OR, 0.84; 95% CI, 0.80-0.89), and in medical patients (OR, 0.94; 95% CI, 0.94-0.95) per additional full time equivalent per patient day. An increase by 1 RN per patient day was associated with a decreased odds ratio of hospital acquired pneumonia (OR, 0.70; 95% CI, 0.56-0.88), unplanned extubation (OR, 0.49; 95% CI, 0.36-0.67), respiratory failure (OR, 0.40; 95% CI, 0.27-0.59), and cardiac arrest (OR, 0.72; 95% CI, 0.62-0.84) in ICUs, with a lower risk of failure to rescue (OR, 0.84; 95% CI, 0.79-0.90) in surgical patients. Length of stay was shorter by 24% in ICUs (OR, 0.76; 95% CI, 0.62-0.94) and by 31% in surgical patients (OR, 0.69; 95% CI, 0.55-0.86).Studies with different design show associations between increased RN staffing and lower odds of hospital related mortality and adverse patient events. Patient and hospital characteristics, including hospitals' commitment to quality of medical care, likely contribute to the actual causal pathway.

Failure to rescue (FTR), the rate of death in patients suffering 1 of 6 in-hospital complications, is an Agency for Healthcare Research and Quality (AHRQ) Patient Safety Indicator calculated from administrative data.: We sought to assess the accuracy of the AHRQ FTR algorithm.We undertook a retrospective chart review of 60 denominator cases of FTR identified by the algorithm at each of 40 University HealthSystem Consortium institutions. The primary outcome was the overall accuracy of the algorithm compared with chart review. We also assessed accuracy by complication type, patient characteristics, institution, service assignment, and mortality.Of 2354 cases, 1193 (50.7%) were accurately identified by the algorithm as having had at least one of the FTR-qualifying complications during hospitalization. Of the 3073 complications identified in these patients, 1497 (48.7%) were correctly flagged by the algorithm, 907 (29.5%) were present on admission, 419 (13.6%) were not confirmed by chart review, and 250 (8.1%) met a predefined complication-specific criterion for exclusion. The case accuracy rate varied significantly by institution (mean, 50.7%; range, 18.3-100%; P0.001), service assignment (surgical service, 62.9% vs. nonsurgical service, 42.9%; P0.001), and mortality (alive, 43.9% vs. dead, 67.5%; P0.001) but was not affected by patients' age, gender, race, or insurance status.As currently calculated from administrative data, the FTR algorithm misidentifies half of the cases on average, is least accurate for nonsurgical cases, and is widely variable across institutions. This indicator may be useful internally to flag possible cases of quality failure but has limitations for external institutional comparisons. Improvements in coding quality and consistency across institutions are needed.

Introduction The optimal methodology for assessing comorbidity to predict various surgical outcomes such as mortality, readmissions, complications, and failure to rescue (FTR) using claims data has not been established. Objective Compare diagnosis-based and prescription-based comorbidity scores for predicting surgical outcomes. Methods We used 100% Texas Medicare data (2006-2011) and included patients undergoing coronary artery bypass grafting, pulmonary lobectomy, endovascular repair of abdominal aortic aneurysm, open repair of abdominal aortic aneurysm, colectomy, and hip replacement (N=39,616). The ability of diagnosis-based [Charlson comorbidity score, Elixhauser comorbidity score, Combined Comorbidity Score, Centers for Medicare and Medicaid Services-Hierarchical Condition Categories (CMS-HCC)] versus prescription-based Chronic disease score in predicting 30-day mortality, 1-year mortality, 30-day readmission, complications, and FTR were compared using c-statistics (c) and integrated discrimination improvement (IDI). Results The overall 30-day mortality was 5.8%, 1-year mortality was 17.7%, 30-day readmission was 14.1%, complication rate was 39.7%, and FTR was 14.5%. CMS-HCC performed the best in predicting surgical outcomes (30-d mortality, c=0.797, IDI=4.59%; 1-y mortality, c=0.798, IDI=9.60%; 30-d readmission, c=0.630, IDI=1.27%; complications, c=0.766, IDI=9.37%; FTR, c=0.811, IDI=5.24%) followed by Elixhauser comorbidity index/disease categories (30-d mortality, c=0.750, IDI=2.37%; 1-y mortality, c=0.755, IDI=5.82%; 30-d readmission, c=0.629, IDI=1.43%; complications, c=0.730, IDI=3.99%; FTR, c=0.749, IDI=2.17%). Addition of prescription-based scores to diagnosis-based scores did not improve performance. Conclusions The CMS-HCC had superior performance in predicting surgical outcomes. Prescription-based scores, alone or in addition to diagnosis-based scores, were not better than any diagnosis-based scoring system.

I ust as continuous quality improvement is needed for the highly complex world of health I care delivery, measurement of quality itself merits continual examination. With the price tag for health care rising and increased public awareness of medical error, pressures continue to mount for ways to efficiently measure the outcomes of health care. An alphabet soup of organizations are developing, evaluating, and implementing such measures for a variety of purposes. However, without sound validation studies and careful interpretation of their implications, the science of measurement and the appropriate usage of measures are stymied. Therefore, the validation study by Horwitz et al, conducted with members of the University HealthSystem Consortium (UHC), is a welcome addition to the scientific literature. This editorial offers a careful examination of the study and its implications, so that the sound bites inevitably culled from its analysis and conclusions do not obscure the essential truths. Drawing from a familiar metaphor the baby and the bathwater the voices that quickly seize any reason to dismiss an indicator, must be averted to preserve that which is particularly valuable in the tub: (1) the concept of failure to rescue (FTR), (2) use of administrative data, (3) chart review validation, and (4) prudent interpretation of the validation study.

Background The Agency for Healthcare Research and Quality (AHRQ) patient safety indicator "death among surgical inpatients with serious treatable complications" (failure-to-rescue) uses rules to exclude complications presumed to be present-on-admission (POA). Like other administrative data-based quality measures, exclusion rules were developed with limited information on whether complications were POA. We examine whether the accuracy of failure-to-rescue exclusion rules can be improved with data with good POA indicators. Methods POA-coded data from 243,825 discharges from a large academic medical center were used to develop 3 failure-to-rescue exclusion rules. Data from 82,871 discharges from California hospitals screened for good POA coding practices was used as a validation sample. The AHRQ failure-to-rescue measure and 3 new measures based on alternative exclusion rules were compared on sensitivity, specificity, and C-statistics for prediction of POA status. Using data from the AHRQ HCUP National Inpatient Sample, the alternative specifications were tested for sensitivity to nurse staffing. Results The AHRQ exclusion rules had sensitivity of 18.5%, specificity 92.1%, and a C-statistic of 0.553. All POA-informed specifications of exclusion rules improved the C-statistic of the failure-to-rescue measure and its sensitivity, with modest losses of specificity. For all tested specifications, higher licensed hours and proportions of registered nurse were statistically significant and associated with lower risk of death. Conclusions Failure-to-rescue is a robust quality measure, sensitive to nursing across alternative exclusion rule specifications. Despite expanded POA coding, exclusion-based rules are needed to analyze datasets not coded for POA, legacy datasets, and datasets with poor POA coding. POA-informed construction of exclusions significantly improves rules identifying POA complications.

Use of failure-to-rescue (FTR) as an indicator of hospital quality has increased over the past decade, but recent authors have used different sets of complications and deaths to define this measure. This study examines the reliability and validity of different FTR measures currently in use.We studied 3 definitions: (1) "original" FTR (using all deaths); (2) FTR-N, a "nursing sensitive" definition that uses only specific complications and deaths; and (3) FTR-A [another restricted definition of FTR used by Agency for Healthcare Research and Quality (AHRQ) for analyzing Healthcare Cost and Utilization Project (HCUP) data]. Each FTR measure was applied to 403,679 general surgical patients across 1567 hospitals reported in 1999-2000 Medicare MEDPAR data.Although FTR used all deaths, FTR-N and FTR-A definitions omitted 49% and 42% of deaths, respectively. Reliability was better for FTR than FTR-A or FTR-N (rho = 0.32 vs. 0.18 vs. 0.18, respectively).Hospitals ranked by adjusted mortality were highly correlated with FTR (Kendall's tau = 0.83) and less correlated with FTR-A (tau = 0.43) and FTR-N (tau = 0.41). Adjusting for patient characteristics, all FTR measures showed strong associations with bed-to-nurse ratio, nursing mix, teaching status, and hospital size; however, hospital "high technology" was not as well associated with FTR-N.For general surgery, more limited definitions used by FTR-N and FTR-A omit over 40% of deaths, display less reliability, and may have more questionable validity than the original FTR measure. We encourage analysts to use the original FTR definition that uses all deaths when analyzing hospital quality of care.

We asked if the factors that predict overall mortality following two common surgical procedures are different from those that predict adverse occurrences (complications) during the hospitalization or death after an adverse occurrence, which we refer to as "failure to rescue." We examined 5,972 Medicare patients undergoing elective cholecystectomy or transurethral prostatectomy using three outcome measures: 1) the death rate (number of deaths/number of patients); 2) the adverse occurrence rate (number of patients who developed an adverse occurrence/number of patients); and 3) the failure rate (number of deaths in patients who developed an adverse occurrence/number of patients with an adverse occurrence). The death rate was associated with both hospital and patient characteristics. The adverse occurrence rate was associated primarily with patient characteristics. In contrast, failure to rescue was associated more with hospital characteristics, and was less influenced by patient admission severity of illness as measured by the MedisGroups score. We concluded that factors associated with hospital failure to rescue are different from factors associated with adverse occurrences or death. Understanding the reasons behind variation in mortality rates across hospitals should improve our ability to use mortality statistics to help hospitals upgrade the quality of care.