Harmon S. Jordan

Combining administrative and clinical data to stratify surgical risk.

Objective:To evaluate whether administrative claims data (ADM) from hospital discharges can be transformed by present-on-admission (POA) codes and readily available clinical data into a refined database that can support valid risk stratification (RS) of surgical outcomes. Summary Background Data:ADM from hospital discharges have been used for RS of medical and surgical outcomes, but results generally have been viewed with skepticism because of limited clinical information and questionable predictive accuracy. Methods:We used logistic regression analysis to choose predictor variables for RS of mortality in abdominal aortic aneurysm repair, coronary artery bypass graft surgery, and craniotomy, and for RS of 4 postoperative complications (ie, physiologic/metabolic derangement, respiratory failure, pulmonary embolism/deep vein thrombosis, and sepsis) after selected operations. RS models were developed for age only (Age model), ADM only (ADM model), ADM enhanced with POA codes for secondary diagnoses (POA-ADM model), POA-ADM supplemented with admission laboratory data (Laboratory model), Laboratory model supplemented with admission vital signs and additional laboratory data (VS model), VS model supplemented with key clinical findings abstracted from medical records (KCF model), and KCF model supplemented with composite clinical scores (Full model). Models were evaluated using c-statistics, case-based errors in predictions, and measures of hospital-based systematic bias. Results:The addition of POA codes and numerical laboratory results to ADM was associated with substantial improvements in all measures of analytic performance. In contrast, the addition of difficult-to-obtain key clinical findings resulted in only small improvements in predictions. Conclusions:Enhancement of ADM with POA codes and readily available laboratory data can efficiently support accurate risk-stratified measurements of clinical outcomes in surgical patients.

Clinical and biological moderators of response to naltrexone in alcohol dependence: a systematic review of the evidence

AIM The goal of this systematic review was to identify moderators of naltrexone efficacy in the treatment of alcohol dependence. METHODS We searched Pubmed, CINHAL, Embase, PsycINFO and the Cochrane Library from 1990 to April 2012 and reference lists of pertinent review articles, which yielded 622 trial, pooled analysis and review articles. Using pre-established eligibility criteria, two reviewers independently determined whether abstracts contained evidence of demographic or biological characteristics, i.e. moderators, influencing naltrexone response in alcohol dependence. We assessed each publication for risk of bias and evaluated the strength of the body of evidence for each moderator. RESULTS Twenty-eight publications (on 20 studies) met criteria for data synthesis. These included 26 publications from 12 randomized, placebo-controlled trials, three non-randomized, non-placebo studies and one randomized, non-placebo study. In addition, there were two publications from pooled analyses of four randomized, placebo-controlled trials. Family history of alcohol problems and the Asn40Asp polymorphism of the μ-opioid receptor gene showed a positive association with efficacy in four of five and three of five studies, respectively. Other moderators reported to be associated with efficacy included male sex (two of five studies), pre-treatment drinking (two of two studies) and high craving (two of five studies). However, the overall risk of bias in the published literature is high. CONCLUSIONS The identification of naltrexone-responsive alcohol-dependent patients is still in development. Studies to date point to two potential moderators-family history and presence of the OPRM1 Asn40Asp polymorphism-as having the strongest evidence. However, the data to date is still insufficient to recommend that any moderator be used in determining clinical treatment.

Modifying ICD-9-CM Coding of Secondary Diagnoses to Improve Risk-Adjustment of Inpatient Mortality Rates

Objective . To assess the effect on risk-adjustment of inpatient mortality rates of progressively enhancing administrative claims data with clinical data that are increasingly expensive to obtain. Data Sources . Claims and abstracted clinical data on patients hospitalized for 5 medical conditions and 3 surgical procedures at 188 Pennsylvania hospitals from July 2000 through June 2003. Methods . Risk-adjustment models for inpatient mortality were derived using claims data with secondary diagnoses limited to conditions unlikely to be hospital-acquired complications. Models were enhanced with one or more of 1) secondary diagnoses inferred from clinical data to have been present-on-admission (POA), 2) secondary diagnoses not coded on claims but documented in medical records as POA, 3) numerical laboratory results from the first hospital day, and 4) all available clinical data from the first hospital day. Alternative models were compared using c-statistics, the magnitude of errors in prediction for individual cases, and the percentage of hospitals with aggregate errors in prediction exceeding specified thresholds. Results . More complete coding of a few under-reported secondary diagnoses and adding numerical laboratory results to claims data substantially improved predictions of inpatient mortality. Little improvement resulted from increasing the maximum number of available secondary diagnoses or adding additional clinical data. Conclusions . Increasing the completeness and consistency of reporting a few secondary diagnosis codes for findings POA and merging claims data with numerical laboratory values improved risk adjustment of inpatient mortality rates. Expensive abstraction of additional clinical information from medical records resulted in little further improvement.

Piloting nursing-sensitive hospital care measures in Massachusetts.

Under the umbrella of the Massachusetts Hospital Association and Massachusetts Organization of Nurse Executives Patients First Initiative, Massachusetts hospitals tested a subset of NQF-endorsed nursing-sensitive care measures in 2006. In this report, we describe the pilot test, report on pilot test measure data, summarize participant feedback on the tested measures, and offer observations on lessons learned from the pilot test.

Cost-effective enhancement of claims data to improve comparisons of patient safety

Tools that support screening for medical errors can help to identify potential patient safety events for further investigation and can provide benchmarks against which providers, localities, and states can compare themselves. The Agency for Healthcare Research and Quality Patient Safety Indicators, which are based solely on hospital administrative or claims data, represent one such tool. Without sufficient clinical detail, measures based on claims data may not accurately reflect hospital quality of care. To construct risk-adjustment models, we used hospital discharge data from July 2000 to June 2003 from 188 Pennsylvania hospitals supplied by the Pennsylvania Health Care Cost Containment Council. We augmented the hospital claims data with clinical data (also supplied by the Pennsylvania Health Care Cost Containment Council) abstracted from medical records using MediQuals proprietary Atlas™ (MediQual, Westborough, MA, a subsidiary of CardinalHealth) clinical information system. Clinical data elements included such items as patient history, laboratory results, vital signs, and other clinical findings. Our cost-effectiveness analyses strongly support the value of enhancing administrative claims data with a present-on-admission code and adding a limited set of numerical laboratory values. Reasonable additional benefit may be gained by adding vital signs to this data set, but the trade-off between effectiveness and cost is not as clear. Also, more accurate International Classification of Diseases, Ninth Revision, Clinical Modification coding of specific secondary diagnoses that are currently undercoded could improve the validity of risk-adjustment equations without the added cost of abstracting clinical findings from medical records. There seems to be little justification for secondary abstraction of medical records to obtain data for risk-adjusting the Agency for Healthcare Research and Quality Patient Safety Indicators.

Linking pharmacoeconomic analyses to results of systematic review and meta-analysis

Pharmacoeconomic analysis applies quantitative modeling to the assessment of the clinical and economic impact of new drugs. Users of pharmacoeconomic analysis include government agencies, government payers and policy makers, private payers (including managed care organizations) and pharmaceutical companies. Pharmacoeconomic analyses can aid policy decisions, provide support for better allocation of scarce resources and assist clinical decisions. Since pharmacoeconomic analyses can have a wide impact, it is important that they are based upon reliable data. Well-conducted systematic reviews and meta-analyses can provide high quality data to pharmacoeconomic analyses, with considerable synergy achieved by combining these two powerful methodologies. An overview of systematic review and meta-analysis are presented and some examples of their use in pharmacoeconomics are described.