Timing of Readmissions After Elective Total Hip and Knee Arthroplasty: Does a 30-Day All-Cause Rate Capture Surgically Relevant Readmissions?

BackgroundThe 30-day all-cause readmission rate is a widely used metric of hospital performance. However, there is lack of clarity as to whether 30 days is an appropriate time frame following surgical procedures. Our aim is to determine whether a 90-day time window is superior to a 30-day time window in capturing surgically relevant readmissions after total hip arthroplasty (THA) and total knee arthroplasty (TKA).MethodsWe analyzed readmissions following all primary THAs and TKAs recorded in the English National Health Service Hospital Episode Statistics database from 2008 to 2018. We compared temporal patterns of 30- and 90-day readmission rates for the following types of readmission: all-cause, surgical, return to theater, and those related to specific surgical complications.ResultsA total of 1.47 million procedures were recorded. After THA and TKA, over three-quarters of 90-day surgical readmissions took place within the first 30 days (78.5% and 75.7%, respectively). All-cause and surgical readmissions both peaked at day 4 and followed a similar temporal course thereafter. The ratio of surgical to medical readmissions was greater for THA than for TKA, with THA dislocation both being one of the most common surgical complications and clustering early after discharge, with 73.5% of 90-day dislocations occurring within the first 30 days.ConclusionThe 30-day all-cause readmission rate is a good reflection of surgically relevant readmissions that take place in the first 90 days after THA and TKA.     

Is the placement of jejunostomy tubes in patients with esophageal cancer undergoing esophagectomy associated with increased inpatient healthcare utilization? An analysis of the National Readmissions Database

BackgroundPatients undergoing esophagectomy often receive jejunostomy tubes (j-tubes) for nutritional supplementation. We hypothesized that j-tubes are associated with increased post-esophagectomy readmissions.Study designWe identified esophagectomies for malignancy with (EWJ) or without (EWOJ) j-tubes using the 2010–2015 Nationwide Readmissions Database. Outcomes include readmission, inpatient mortality, and complications. Outcomes were compared before and after propensity score matching (PSM).ResultsOf 22,429 patients undergoing esophagectomy, 16,829 (75.0%) received j-tubes. Patients were similar in age and gender but EWJ were more likely to receive chemotherapy (24.2% vs. 15.1%, p < 0.01). EWJ was associated with decreased 180-day inpatient mortality (HR 0.72 [0.52–0.99]) but not with higher readmissions at 30- (15.2% vs. 14.0%, p = 0.16; HR 0.9 [0.77–1.05]) or 180 days (25.2% vs. 24.3%, p = 0.37; HR 0.94 [0.79–1.10]) or increased complications (p = 0.37). These results were confirmed in the PSM cohort.ConclusionJ-tubes placed in the setting of esophagectomy do not increase inpatient readmissions or mortality.     

Comorbid depression in surgical cancer patients associated with non-routine discharge and readmission

ObjectiveTo characterize the rates of depression across primary cancer sites, and determine the effects of comorbid depression among surgical cancer patients on established quality of care indicators, non-routine discharge and readmission.MethodsPatients undergoing surgical resection for cancer were selected from the Nationwide Readmissions Database (2010–2014). Multivariable analysis adjusted for patient and hospital level characteristics to ascertain the effect of depression on post-operative outcomes and 30-day readmission rates. Non-routine discharge encompasses discharge to skilled nursing, inpatient rehabilitation, and intermediate care facilities, as well as discharge home with home health services.ResultsAmong 851,606 surgically treated cancer patients, 8.1% had a comorbid diagnosis of depression at index admission (n = 69,174). Prevalence of depression was highest among patients with cancer of the brain (10.9%), female genital organs (10.9%), and lung (10.5%), and lowest among those with prostate cancer (4.9%). Depression prevalence among women (10.9%) was almost twice that of men (5.7%). Depression was associated with non-routine discharge after surgery (OR 1.20, CI:1.18–1.23, p < 0.0001*) and hospital readmission within 30 days (OR 1.12, CI:1.09–1.15, p < 0.001*).ConclusionRates of depression vary amongst surgically treated cancer patients by primary tumor site. Comorbid depression in these patients is associated with increased likelihood of non-routine discharge and readmission.     

Beyond discrimination: A comparison of calibration methods and clinical usefulness of predictive models of readmission risk

BackgroundPrior to implementing predictive models in novel settings, analyses of calibration and clinical usefulness remain as important as discrimination, but they are not frequently discussed. Calibration is a model’s reflection of actual outcome prevalence in its predictions. Clinical usefulness refers to the utilities, costs, and harms of using a predictive model in practice. A decision analytic approach to calibrating and selecting an optimal intervention threshold may help maximize the impact of readmission risk and other preventive interventions.ObjectivesTo select a pragmatic means of calibrating predictive models that requires a minimum amount of validation data and that performs well in practice. To evaluate the impact of miscalibration on utility and cost via clinical usefulness analyses.Materials and methodsObservational, retrospective cohort study with electronic health record data from 120,000 inpatient admissions at an urban, academic center in Manhattan. The primary outcome was thirty-day readmission for three causes: all-cause, congestive heart failure, and chronic coronary atherosclerotic disease. Predictive modeling was performed via L1-regularized logistic regression. Calibration methods were compared including Platt Scaling, Logistic Calibration, and Prevalence Adjustment. Performance of predictive modeling and calibration was assessed via discrimination (c-statistic), calibration (Spiegelhalter Z-statistic, Root Mean Square Error [RMSE] of binned predictions, Sanders and Murphy Resolutions of the Brier Score, Calibration Slope and Intercept), and clinical usefulness (utility terms represented as costs). The amount of validation data necessary to apply each calibration algorithm was also assessed.ResultsC-statistics by diagnosis ranged from 0.7 for all-cause readmission to 0.86 (0.78–0.93) for congestive heart failure. Logistic Calibration and Platt Scaling performed best and this difference required analyzing multiple metrics of calibration simultaneously, in particular Calibration Slopes and Intercepts. Clinical usefulness analyses provided optimal risk thresholds, which varied by reason for readmission, outcome prevalence, and calibration algorithm. Utility analyses also suggested maximum tolerable intervention costs, e.g., $1720 for all-cause readmissions based on a published cost of readmission of $11,862.ConclusionsChoice of calibration method depends on availability of validation data and on performance. Improperly calibrated models may contribute to higher costs of intervention as measured via clinical usefulness. Decision-makers must understand underlying utilities or costs inherent in the use-case at hand to assess usefulness and will obtain the optimal risk threshold to trigger intervention with intervention cost limits as a result.     

Community Factors and Hospital Readmission Rates

Objective

To examine the relationship between community factors and hospital readmission rates.

Data Sources/Study Setting

We examined all hospitals with publicly reported 30-day readmission rates for patients discharged during July 1, 2007, to June 30, 2010, with acute myocardial infarction (AMI), heart failure (HF), or pneumonia (PN). We linked these to publicly available county data from the Area Resource File, the Census, Nursing Home Compare, and the Neilsen PopFacts datasets.

Study Design

We used hierarchical linear models to assess the effect of county demographic, access to care, and nursing home quality characteristics on the pooled 30-day risk-standardized readmission rate.

Data Collection/Extraction Methods

Not applicable.

Principal Findings

The study sample included 4,073 hospitals. Fifty-eight percent of national variation in hospital readmission rates was explained by the county in which the hospital was located. In multivariable analysis, a number of county characteristics were found to be independently associated with higher readmission rates, the strongest associations being for measures of access to care. These county characteristics explained almost half of the total variation across counties.

Conclusions

Community factors, as measured by county characteristics, explain a substantial amount of variation in hospital readmission rates.