Impact of Present-on-admission Indicators on Risk-adjusted Hospital Mortality Measurement

* Senior Biostatistician, Department of Quantitative Health Sciences and Department of Outcomes Research, Cleveland Clinic, Cleveland, Ohio, and Department of Epidemiology and Biostatistics, Case Western Reserve University, Cleveland, Ohio. † Professor, Department of Anesthesiology and Community and Preventive Medicine, University of Rochester School of Medicine, Rochester, New York. ‡ Associate Staff, Department of Quantitative Health Sciences and Department of Outcomes Research, Cleveland Clinic. § Assistant Staff, Department of Quantitative Health Sciences, Cleveland Clinic. ‖ Adjunct Staff, Department of Outcomes Research, Cleveland Clinic. # Michael Cudahy Professor and Chair, Department of Outcomes Research, Cleveland Clinic.
Anesthesiology (Impact Factor: 6.17). 03/2013; 118(6). DOI: 10.1097/ALN.0b013e31828e12b3
Source: PubMed

ABSTRACT BACKGROUND:: Benchmarking performance across hospitals requires proper adjustment for differences in baseline patient and procedural risk. Recently, a Risk Stratification Index was developed from Medicare data, which used all diagnosis and procedure codes associated with each stay, but did not distinguish present-on-admission (POA) diagnoses from hospital-acquired diagnoses. We sought to (1) develop and validate a risk index for in-hospital mortality using only POA diagnoses, principal procedures, and secondary procedures occurring before the date of the principal procedure (POARisk) and (2) compare hospital performance metrics obtained using the POARisk model with those obtained using a similarly derived model which ignored the timing of diagnoses and procedures (AllCodeRisk). METHODS:: We used the 2004-2009 California State Inpatient Database to develop, calibrate, and prospectively test our models (n = 24 million). Elastic net logistic regression was used to estimate the two risk indices. Agreement in hospital performance under the two respective risk models was assessed by comparing observed-to-expected mortality ratios; acceptable agreement was predefined as the AllCodeRisk-based observed-to-expected ratio within ±20% of the POARisk-based observed-to-expected ratio for more than 95% of hospitals. RESULTS:: After recalibration, goodness of fit (i.e., model calibration) within the 2009 data was excellent for both models. C-statistics were 0.958 and 0.981, respectively, for the POARisk and AllCodeRisk models. The AllCodeRisk-based observed-to-expected ratio was within ±20% of the POARisk-based observed-to-expected ratio for 89% of hospitals, which was slightly lower than the predefined limit of agreement. CONCLUSION:: Consideration of POA coding meaningfully improved hospital performance measurement. The POARisk model should be used for risk adjustment when POA data are available.

  • [Show abstract] [Hide abstract]
    ABSTRACT: Perioperative myocardial infarction (PMI) is a major surgical complication that is costly and causes much morbidity and mortality. Diagnosis and treatment of PMIs have evolved over time. Many treatments are expensive but may reduce ancillary expenses including the duration of hospital stay. The time-dependent economic impact of novel treatments for PMI remains unexplored. The authors thus evaluated absolute and incremental costs of PMI over time and discharge patterns. Approximately 31 million inpatient discharges were analyzed between 2003 and 2010 from the California State Inpatient Database. PMI was defined using International Classification of Diseases, Ninth Revision, Clinical Modification codes. Propensity matching generated 21,637 pairs of comparable patients. Quantile regression modeled incremental charges as the response variable and year of discharge as the main predictor. Time trends of incremental charges adjusted to 2012 dollars, mortality, and discharge destination was evaluated. Median incremental charges decreased annually by $1,940 (95% CI, $620 to $3,250); P < 0.001. Compared with non-PMI patients, the median length of stay of patients who experienced PMI decreased significantly over time: yearly decrease was 0.16 (0.10 to 0.23) days; P < 0.001. No mortality differences were seen; but over time, PMI patients were increasingly likely to be transferred to another facility. Reduced incremental cost and unchanged mortality may reflect improving efficiency in the standard management of PMI. An increasing fraction of discharges to skilled nursing facilities seems likely a result from hospitals striving to reduce readmissions. It remains unclear whether this trend represents a transfer of cost and risk or improves patient care.
    Anesthesiology 03/2014; 121(1). DOI:10.1097/ALN.0000000000000233 · 6.17 Impact Factor
  • Source
    International anesthesiology clinics 01/2013; 51(4):10-21. DOI:10.1097/AIA.0b013e3182a70a52
  • Anaesthesia 02/2014; 69(2):100-5. DOI:10.1111/anae.12537 · 3.85 Impact Factor