Patricia Halfon

Coding algorithms for defining comorbidities in ICD-9-CM and ICD-10 administrative data.

Objectives:Implementation of the International Statistical Classification of Disease and Related Health Problems, 10th Revision (ICD-10) coding system presents challenges for using administrative data. Recognizing this, we conducted a multistep process to develop ICD-10 coding algorithms to define Charlson and Elixhauser comorbidities in administrative data and assess the performance of the resulting algorithms. Methods:ICD-10 coding algorithms were developed by “translation” of the ICD-9-CM codes constituting Deyos (for Charlson comorbidities) and Elixhausers coding algorithms and by physicians’ assessment of the face-validity of selected ICD-10 codes. The process of carefully developing ICD-10 algorithms also produced modified and enhanced ICD-9-CM coding algorithms for the Charlson and Elixhauser comorbidities. We then used data on in-patients aged 18 years and older in ICD-9-CM and ICD-10 administrative hospital discharge data from a Canadian health region to assess the comorbidity frequencies and mortality prediction achieved by the original ICD-9-CM algorithms, the enhanced ICD-9-CM algorithms, and the new ICD-10 coding algorithms. Results:Among 56,585 patients in the ICD-9-CM data and 58,805 patients in the ICD-10 data, frequencies of the 17 Charlson comorbidities and the 30 Elixhauser comorbidities remained generally similar across algorithms. The new ICD-10 and enhanced ICD-9-CM coding algorithms either matched or outperformed the original Deyo and Elixhauser ICD-9-CM coding algorithms in predicting in-hospital mortality. The C-statistic was 0.842 for Deyos ICD-9-CM coding algorithm, 0.860 for the ICD-10 coding algorithm, and 0.859 for the enhanced ICD-9-CM coding algorithm, 0.868 for the original Elixhauser ICD-9-CM coding algorithm, 0.870 for the ICD-10 coding algorithm and 0.878 for the enhanced ICD-9-CM coding algorithm. Conclusions:These newly developed ICD-10 and ICD-9-CM comorbidity coding algorithms produce similar estimates of comorbidity prevalence in administrative data, and may outperform existing ICD-9-CM coding algorithms.

Atherogenic Dyslipidemia in HIV-Infected Individuals Treated With Protease Inhibitors

BACKGROUND Administration of protease inhibitors (PIs) to HIV-infected individuals has been associated with hyperlipidemia. In this study, we characterized the lipoprotein profile in subjects receiving ritonavir, indinavir, or nelfinavir, alone or in combination with saquinavir. METHODS AND RESULTS Plasma lipoprotein levels were quantified in 93 HIV-infected adults receiving PIs. Comparison was done with pretreatment values and with 28 nonPI-treated HIV-infected subjects. An elevation in plasma cholesterol levels was observed in all PI-treated groups but was more pronounced for ritonavir (2.0+/-0.3 mmol/L [mean+/-SEM], n=46, versus 0.1+/-0.2 mmol/L in nonPI treated group, P<0.001) than for indinavir (0.8+/-0.2 mmol/L, n=26, P=0.03) or nelfinavir (1.2+/-0.2 mmol/L, n=21, P=0.01). Administration of ritonavir, but not indinavir or nelfinavir, was associated with a marked elevation in plasma triglyceride levels (1.83+/-0.46 mmol/L, P=0.002). Plasma HDL-cholesterol levels remained unchanged. Combination of ritonavir or nelfinavir with saquinavir did not further elevate plasma lipid levels. A 48% increase in plasma levels of lipoprotein(a) was detected in PI-treated subjects with pretreatment Lp(a) values >20 mg/dL. Similar changes in plasma lipid levels were observed in 6 children receiving ritonavir. CONCLUSIONS Administration of PIs to HIV-infected individuals is associated with a marked, compound-specific dyslipidemia. The risk of pancreatitis and premature atherosclerosis due to PI-associated dyslipidemia remains to be established.

Validation of the potentially avoidable hospital readmission rate as a routine indicator of the quality of hospital care.

Background:The hospital readmission rate has been proposed as an important outcome indicator computable from routine statistics. However, most commonly used measures raise conceptual issues. Objectives:We sought to evaluate the usefulness of the computerized algorithm for identifying avoidable readmissions on the basis of minimum bias, criterion validity, and measurement precision. Research Design and Subjects:A total of 131,809 hospitalizations of patients discharged alive from 49 hospitals were used to compare the predictive performance of risk adjustment methods. A subset of a random sample of 570 medical records of discharge/readmission pairs in 12 hospitals were reviewed to estimate the predictive value of the screening of potentially avoidable readmissions. Measures:Potentially avoidable readmissions, defined as readmissions related to a condition of the previous hospitalization and not expected as part of a program of care and occurring within 30 days after the previous discharge, were identified by a computerized algorithm. Unavoidable readmissions were considered as censored events. Results:A total of 5.2% of hospitalizations were followed by a potentially avoidable readmission, 17% of them in a different hospital. The predictive value of the screen was 78%; 27% of screened readmissions were judged clearly avoidable. The correlation between the hospital rate of clearly avoidable readmission and all readmissions rate, potentially avoidable readmissions rate or the ratio of observed to expected readmissions were respectively 0.42, 0.56 and 0.66. Adjustment models using clinical information performed better. Conclusion:Adjusted rates of potentially avoidable readmissions are scientifically sound enough to warrant their inclusion in hospital quality surveillance.

Risk of falls for hospitalized patients: A predictive model based on routinely available data

The incidence rate of falls is often used as an indicator of nursing care outcome. Comparing outcome between different settings should, however, make allowance for case mix. To measure the incidence of falls, describe their circumstances and develop a prediction model based on routinely collected data to allow comparison between hospital settings with different case mix. A dynamic population of patients hospitalized over a year in which a case was defined as any accidental fall systematically reported on an ad hoc form. A Swiss university hospital of 800 beds; 634 falls were reported for 26,643 hospitalizations over 236,307 hospitalization days. First fall rates were analyzed using a Poisson regression model with routinely computerized discharge data as independent variables. The incidence rate of first falls was 2.2 per 1000 patient-days. For subsequent falls the rates of incidence increased with the number of falls. Five independent variables played a significant role: age, gender, morbidity predisposition, surgical procedure and length of stay. Two of the interactions between these variables were significant and remained in the model (length of stay with age, morbidity with age). The model offers good medical plausibility and satisfactory predictive performance. The proposed model can be used by national health agencies to compute expected first fall rates accounting for case mix. Hospitals can use these rates for evaluation. Recommendations for measuring, monitoring and assessing fall rates are also given.

Measuring potentially avoidable hospital readmissions

The objectives of this study were to develop a computerized method to screen for potentially avoidable hospital readmissions using routinely collected data and a prediction model to adjust rates for case mix. We studied hospital information system data of a random sample of 3,474 inpatients discharged alive in 1997 from a university hospital and medical records of those (1,115) readmitted within 1 year. The gold standard was set on the basis of the hospital data and medical records: all readmissions were classified as foreseen readmissions, unforeseen readmissions for a new affection, or unforeseen readmissions for a previously known affection. The latter category was submitted to a systematic medical record review to identify the main cause of readmission. Potentially avoidable readmissions were defined as a subgroup of unforeseen readmissions for a previously known affection occurring within an appropriate interval, set to maximize the chance of detecting avoidable readmissions. The computerized screening algorithm was strictly based on routine statistics: diagnosis and procedures coding and admission mode. The prediction was based on a Poisson regression model. There were 454 (13.1%) unforeseen readmissions for a previously known affection within 1 year. Fifty-nine readmissions (1.7%) were judged avoidable, most of them occurring within 1 month, which was the interval used to define potentially avoidable readmissions (n = 174, 5.0%). The intra-sample sensitivity and specificity of the screening algorithm both reached approximately 96%. Higher risk for potentially avoidable readmission was associated with previous hospitalizations, high comorbidity index, and long length of stay; lower risk was associated with surgery and delivery. The model offers satisfactory predictive performance and a good medical plausibility. The proposed measure could be used as an indicator of inpatient care outcome. However, the instrument should be validated using other sets of data from various hospitals.

Cross-national comparative performance of three versions of the ICD-10 Charlson index.

Objective:The Charlson comorbidity index has been widely used for risk adjustment in outcome studies using administrative health data. Recently, 3 International Statistical Classification of Diseases, Tenth Revision (ICD-10) translations have been published for the Charlson comorbidities. This study was conducted to compare the predictive performance of these versions (the Halfon, Sundararajan, and Quan versions) of the ICD-10 coding algorithms using data from 4 countries. Methods:Data from Australia (N = 2000–2001, max 25 diagnosis codes), Canada (N = 2002–2003, max 16 diagnosis codes), Switzerland (N = 1999–2001, unlimited number of diagnosis codes), and Japan (N = 2003, max 11 diagnosis codes) were analyzed. Only the first admission for patients age 18 years and older, with a length of stay of ≥2 days was included. For each algorithm, 2 logistic regression models were fitted with hospital mortality as the outcome and the Charlson individual comorbidities or the Charlson index score as independent variables. The c-statistic (representing the area under the receiver operating characteristic curve) and its 95% probability bootstrap distribution were employed to evaluate model performance. Results:Overall, within each populations data, the distribution of comorbidity level categories was similar across the 3 translations. The Quan version produced slightly higher median c-statistics than the Halfon or Sundararajan versions in all datasets. For example, in Japanese data, the median c-statistics were 0.712 (Quan), 0.709 (Sundararajan), and 0.694 (Halfon) using individual comorbidity coefficients. In general, the probability distributions between the Quan and the Sundararajan versions overlapped, whereas those between the Quan and the Halfon version did not. Conclusions:Our analyses show that all of the ICD-10 versions of the Charlson algorithm performed satisfactorily (c-statistics 0.70–0.86), with the Quan version showing a trend toward outperforming the other versions in all data sets.

Identifying priorities in methodological research using ICD-9-CM and ICD-10 administrative data: report from an international consortium

BackgroundHealth administrative data are frequently used for health services and population health research. Comparative research using these data has been facilitated by the use of a standard system for coding diagnoses, the International Classification of Diseases (ICD). Research using the data must deal with data quality and validity limitations which arise because the data are not created for research purposes. This paper presents a list of high-priority methodological areas for researchers using health administrative data.MethodsA group of researchers and users of health administrative data from Canada, the United States, Switzerland, Australia, China and the United Kingdom came together in June 2005 in Banff, Canada to discuss and identify high-priority methodological research areas. The generation of ideas for research focussed not only on matters relating to the use of administrative data in health services and population health research, but also on the challenges created in transitioning from ICD-9 to ICD-10. After the brain-storming session, voting took place to rank-order the suggested projects. Participants were asked to rate the importance of each project from 1 (low priority) to 10 (high priority). Average ranks were computed to prioritise the projects.ResultsThirteen potential areas of research were identified, some of which represented preparatory work rather than research per se. The three most highly ranked priorities were the documentation of data fields in each countrys hospital administrative data (average score 8.4), the translation of patient safety indicators from ICD-9 to ICD-10 (average score 8.0), and the development and validation of algorithms to verify the logic and internal consistency of coding in hospital abstract data (average score 7.0).ConclusionThe group discussions resulted in a list of expert views on critical international priorities for future methodological research relating to health administrative data. The consortiums members welcome contacts from investigators involved in research using health administrative data, especially in cross-jurisdictional collaborative studies or in studies that illustrate the application of ICD-10.

A conceptual framework for hospital quality management

Most current conceptual frameworks used for hospital quality management exhibit shortcomings, terminology barriers or too much complexity. We propose a rigorous and simple model specific to hospitals, based on four entities (patients, activities, resources and effects) and six levels in order to measure the development of quality management systems. The proposed model is compatible with other substantiated models, robust in coping with falsifiability and provides flexibility to avoid a too unilateral approach.

Comparing potentially avoidable hospitalization rates related to ambulatory care sensitive conditions in Switzerland: the need to refine the definition of health conditions and to adjust for population health status

BackgroundRegional rates of hospitalization for ambulatory care sensitive conditions (ACSC) are used to compare the availability and quality of ambulatory care but the risk adjustment for population health status is often minimal. The objectives of the study was to examine the impact of more extensive risk adjustment on regional comparisons and to investigate the relationship between various area-level factors and the properly adjusted rates.MethodsOur study is an observational study based on routine data of 2 million anonymous insured in 26 Swiss cantons followed over one or two years. A binomial negative regression was modeled with increasingly detailed information on health status (age and gender only, inpatient diagnoses, outpatient conditions inferred from dispensed drugs and frequency of physician visits). Hospitalizations for ACSC were identified from principal diagnoses detecting 19 conditions, with an updated list of ICD-10 diagnostic codes. Co-morbidities and surgical procedures were used as exclusion criteria to improve the specificity of the detection of potentially avoidable hospitalizations. The impact of the adjustment approaches was measured by changes in the standardized ratios calculated with and without other data besides age and gender.Results25% of cases identified by inpatient main diagnoses were removed by applying exclusion criteria. Cantonal ACSC hospitalizations rates varied from to 1.4 to 8.9 per 1,000 insured, per year. Morbidity inferred from diagnoses and drugs dramatically increased the predictive performance, the greatest effect found for conditions linked to an ACSC. More visits were associated with fewer PAH although very high users were at greater risk and subjects who had not consulted at negligible risk. By maximizing health status adjustment, two thirds of the cantons changed their adjusted ratio by more than 10 percent. Cantonal variations remained substantial but unexplained by supply or demand.ConclusionAdditional adjustment for health status is required when using ACSC to monitor ambulatory care. Drug-inferred morbidities are a promising approach.

Hypertension and intra‐operative incidents: a multicentre study of 125 000 surgical procedures in Swiss hospitals*

It is debated whether chronic hypertension increases the risk of cardiovascular incidents during anaesthesia. We studied all elective surgical operations performed in adults under general or regional anaesthesia between 2000 and 2004, in 24 hospitals collecting computerised clinical data on all anaesthetics since 1996. The focus was on cardiovascular incidents, though other anaesthesia‐related incidents were also evaluated. Among 124 939 interventions, 27 881 (22%) were performed in hypertensive patients. At least one cardiovascular incident occurred in 7549 interventions (6% (95% CI 5.9–6.2%)). The average adjusted odds ratio of cardiovascular risk for chronic hypertension was 1.38 (95% CI 1.27–1.49). However, across hospitals, adjusted odd ratios varied from 0.41 up to 2.25. Hypertension did not increase the risk of other incidents. Hypertensive patients are still at risk of intra‐operative cardiovascular incidents, while risk heterogeneity across hospitals, despite taking account of casemix and hospital characteristics, suggests variations in anaesthetic practices.