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Objective: To compare hospital mortality in a cardiac surgery unit with external data and to assess changes in time (patients undergoing surgery in two different periods)., Materials and Methods: Data on risk factors for hospital mortality were collected from clinical records (retrospectively for the first period and prospectively for the second) for all patients undergoing open heart surgery at the Heart Surgery Unit of the University of Turin (Italy) during 1991 and 1995 (n = 1794) and 1999 (n = 892). Comparisons of in-hospital mortality, expressed as Standardized Mortality Ratios (SMR), were adjusted for risk factors defined according to EuroSCORE (European System for Cardiac Operative Risk Evaluation)., Results: In the first and second period, complete information on all the 17 EuroSCORE items was available for 58.3% and 89.6% patients respectively. After exclusion of patients with one or more missing data, observed and expected numbers of death were found to be very similar, with SMRs ranging between 0.82 (isolated bypass in the second period) and 1.06 ("other" surgery in the first period). Mortality was higher among patients with missing data, but at least in 1999 the latter had a limited impact on the overall estimates. Compared to the first period, mortality was reduced during 1999 (from 5.9% to 5.4%), in particular for isolated bypass (from 4.4% to 3.4%)., Conclusions: In the unit under investigation, hospital mortality following heart surgery was similar to that predicted from EuroSCORE and seemed to be lower in 1999 than in 1991-95, particularly for isolated bypass. Incompleteness of data on individual risk factors may have been a source of bias, especially when data were collected retrospectively.

Using discharge abstract data, we analysed hospital mortality comparing four different methods of risk adjustment. All patients discharged from the S. Giovanni Battista (Molinette) hospital in Turin (Italy) between January 1996 and June 1999 (n = 169,746) were classified with All Patient Refined--Diagnosis Related Groups (APR-DRG). A first analysis evaluated the time trend of hospital mortality by semester. A second analysis compared hospital mortality during the last 12 months among eight units of internal medicine (n = 5592). All comparisons were made through logistic regression models. As the quality of discharge abstracts increased during time and showed variation among units with similar patients, all comparisons were repeated using four models, characterised by increasing predictivity and sensitivity to quality of data. In addition to crude comparisons (A), the other models included as risk factors: B) age and emergency admission; C) same as 'B' plus expected mortality by APR-DRG; D) same as 'B' plus expected mortality by APR-DRG and risk of death subclass. If no risk factors were considered (A), hospital mortality showed an increasing trend, with an odds ratio (OR) of 1.02 by semester, with a 95% confidence interval (CI) between 1.01 and 1.03. The association was weakened when age and mode of admission were taken into account (B) and disappeared when the APR-DRG expected mortality was also considered (C) (OR = 1.00; CI = 0.98-1.01). Finally, if the comparisons were adjusted also for the expected mortality by APR-DRG and risk of death subclass (D) a reversed trend appeared (OR = 0.95; CI = 0.94-0.97). The comparison among the units of internal medicine gave discordant results according to the method used to adjust for confounders. The most striking variations were detected for those units with the best and the worst clinical data. The unit with the poorer clinical data (average number of diagnoses per patient = 2.9) showed a crude OR of 1.38 (CI = 0.99-1.93) and an adjusted OR (D) of 1.71 (CI = 1.10-2.66); the unit with the best quality of data (average number of diagnoses per patient = 4.4) changed the OR from 1.55 (CI = 1.06-2.26) (A) to 0.66 (CI = 0.37-1.17) (D). In conclusion, these results confirm the high sensitivity of the APR-DRG classification to the quality of data and, more in general, suggest to be prudent when using powerful instruments like this to assess quality of care, especially if the quality of data among the units compared is less than optimal or not homogeneous.

Data available from the standard hospital discharge database (SDO) allow us to explore differences in health conditions according to different indicators of socioeconomic status (SES). We analysed all the patients aged 30-59, discharged from the S. Giovanni Battista (Molinette) hospital (the main general hospital in Turin, Italy) during three years (1996-1998) (n = 49949). Three health indicators were used as outcomes: a) emergency admission; b) severity of illness (according to the "All Patient Refined DRGs" subclasses); c) hospital mortality. Patients were compared for each outcome according to two different SES indicators: a) level of education; b) employment status. Logistic regression models (both conditional and unconditional) were used to adjust for several potential confounders. Patients with lower education (up to 5 years of schooling), compared to those with 13 or more years of schooling, showed a higher probability of being admitted through the emergency ward (29.1% vs 23.3%), with an odds ratio (OR) = 1.56-95% confidence interval (95% CI) = 1.45-1.68; of being classified in higher severity subclasses of illness (23.3% vs 17.7%, OR = 1.14; 95% CI = 1.07-1.22) and of dying in hospital (2.3% vs 1.6%). However, after adjustment for other prognostic factors (as severity of illness and specific expected mortality), this association disappeared (OR = 1.05, 95% CI = 0.84-1.32). Similar, but somewhat stronger, associations were observed when comparing the unemployed versus the employed. The corresponding figures (ORs; 95% CI) were 1.57 (1.42-1.74) for emergency admission; 1.31 (1.18-1.45) for severity of illness and 1.55 (1.10-2.16) for hospital mortality. In conclusion, this study showed that SES differentials in health are clearly measurable through routine hospital information systems, and documented that patients of low SES, particularly unemployed, experienced a delayed access to hospital, were admitted in poorer general health conditions and had a more unfavourable prognosis.