Composite Measurement of Outcomes in Medicare Inpatient Laparoscopic Cholecystectomy

Selective Cholecystectomy: using an evidence-based prediction model to plan for cholecystectomy

BACKGROUND

Symptomatic gall stones are treated safely and efficiently with laparoscopic cholecystectomy. Conversion to open cholecystectomy may be associated with adverse outcomes. Accurate prediction of conversion should decrease the incidence of conversion and improve patient care.

METHODS

The recent literature on conversion at laparoscopic cholecystectomy is reviewed to identify robust prediction models that are both internally and externally validated.

RESULTS

Two prediction models are identified which meet these criteria.

CONCLUSIONS

The Cairns Prediction Model using nomograms, is an easily applied tool predicting conversion, which is presently in use. Routine use of this tool should decrease conversion, and improve the process of patient consent.

Risk-Adjusted Hospital Outcomes in Medicare Total Joint Replacement Surgical Procedures

BACKGROUND

Comparative measurement of hospital outcomes can define opportunities for care improvement and will assume great importance as alternative payment models for inpatient total joint replacement surgical procedures are introduced. The purpose of this study was to develop risk-adjusted models for Medicare inpatient and post-discharge adverse outcomes in elective lower-extremity total joint replacement and to apply these models for hospital comparison.

METHODS

Hospitals with ≥50 qualifying cases of elective total hip replacement and total knee replacement from the Medicare Limited Data Set database of 2010 to 2012 were studied. Logistic risk models were designed for adverse outcomes of inpatient mortality, prolonged length-of-stay outliers in the index hospitalization, 90-day post-discharge deaths without readmission, and 90-day readmissions after excluding non-related readmissions. For each hospital, models were used to predict total adverse outcomes, the number of standard deviations from the mean (z-scores) for hospital performance, and risk-adjusted adverse outcomes for each hospital.

RESULTS

A total of 253,978 patients who underwent total hip replacement and 672,515 patients who underwent total knee replacement were studied. The observed overall adverse outcome rates were 12.0% for total hip replacement and 11.6% for total knee replacement. The z-scores for 1,483 hospitals performing total hip replacements varied from -5.09 better than predicted to +5.62 poorer than predicted; 98 hospitals were ≥2 standard deviations better than predicted and 142 hospitals were ≥2 standard deviations poorer than predicted. The risk-adjusted adverse outcome rate for these hospitals was 6.6% for the best-decile hospitals and 19.8% for the poorest-decile hospitals. The z-scores for the 2,349 hospitals performing total knee replacements varied from -5.85 better than predicted to +11.75 poorer than predicted; 223 hospitals were ≥2 standard deviations better than predicted and 319 hospitals were ≥2 standard deviations poorer than predicted. The risk-adjusted adverse outcome rate for these hospitals was 6.4% for the best-decile hospitals and 19.3% for the poorest-decile hospitals.

CONCLUSIONS

Risk-adjusted outcomes demonstrate wide variability and illustrate the need for improvement among poorer-performing hospitals for bundled payments of joint replacement surgical procedures.

CLINICAL RELEVANCE

Adverse outcomes are known to occur in the experience of all clinicians and hospitals. The risk-adjusted benchmarking of hospital performance permits the identification of adverse events that are potentially preventable.

Utility of models to predict 28-day or 30-day unplanned hospital readmissions: an updated systematic review

OBJECTIVE

To update previous systematic review of predictive models for 28-day or 30-day unplanned hospital readmissions.

DESIGN

Systematic review.

SETTING/DATA SOURCE

CINAHL, Embase, MEDLINE from 2011 to 2015.

PARTICIPANTS

All studies of 28-day and 30-day readmission predictive model.

OUTCOME MEASURES

Characteristics of the included studies, performance of the identified predictive models and key predictive variables included in the models.

RESULTS

Of 7310 records, a total of 60 studies with 73 unique predictive models met the inclusion criteria. The utilisation outcome of the models included all-cause readmissions, cardiovascular disease including pneumonia, medical conditions, surgical conditions and mental health condition-related readmissions. Overall, a wide-range C-statistic was reported in 56/60 studies (0.21-0.88). 11 of 13 predictive models for medical condition-related readmissions were found to have consistent moderate discrimination ability (C-statistic ≥0.7). Only two models were designed for the potentially preventable/avoidable readmissions and had C-statistic >0.8. The variables 'comorbidities', 'length of stay' and 'previous admissions' were frequently cited across 73 models. The variables 'laboratory tests' and 'medication' had more weight in the models for cardiovascular disease and medical condition-related readmissions.

CONCLUSIONS

The predictive models which focused on general medical condition-related unplanned hospital readmissions reported moderate discriminative ability. Two models for potentially preventable/avoidable readmissions showed high discriminative ability. This updated systematic review, however, found inconsistent performance across the included unique 73 risk predictive models. It is critical to define clearly the utilisation outcomes and the type of accessible data source before the selection of the predictive model. Rigorous validation of the predictive models with moderate-to-high discriminative ability is essential, especially for the two models for the potentially preventable/avoidable readmissions. Given the limited available evidence, the development of a predictive model specifically for paediatric 28-day all-cause, unplanned hospital readmissions is a high priority.