This study used a six-year dataset on a Swiss multi-million-patient population to explore Charlson and Elixhauser comorbidities’ capacities to predict in-hospital mortality. We first derived a set of optimal population-based weightings for the 31 Elixhauser comorbidities using the national inpatient dataset. Our population-based comorbidity weights were validated – along with the other two weighting systems in the validation sample of Swiss national inpatient data – indicated in-hospital mortality risks with greater sensitivity than either of these weighting systems. The new weighting set had a robust association with in-hospital mortality and discriminated equally well in the derivation and validation groups.
Comparing predictivity regarding in-hospital mortality, the optimized population-based weightings performed slightly better than the Charlson and Elixhauser-van Walraven sets. However, it also supplied weights for eight Elixhauser comorbidities (e.g. diabetes, hypertension, and psychosis) eliminated by van Walraven et al. (2009) . Of the risk-associated comorbidities retained in both the van Walraven and the population-based weights, several comorbidities showed similar results, e.g., the highest odds ratios to metastatic cancer and liver disease. And regarding the comorbidities with negative associations, only slight differences were observed between the van Walraven and population-based weights (e.g., hypothyroidism or obesity were likely to be healthier, and have better survival).
From an epidemiological perspective, chronic diseases such as cancer, heart and liver disease increase the risk of dying in hospitals and certain other disorders, e.g., anaemia and hypothyroidism, actually reduce that risk. These results are in line with those of Zellweger et al.’s  study using the Swiss national death registry of hospital inpatient data from 2010–2012. Furthermore, van Walraven et al.’s  via a single Canadian hospital’s records and Thompson et al.,  using Maryland State inpatient data, showed similar results. These relations could insight the global burden of in-hospital mortality is due to the rising chronic diseases.
The existing weighting systems [11, 25, 21, 14] represent data from a specific geographical region, patient group, or even limited numbers of hospitals or settings, matching the generalizability of these weighting systems remained difficult. As this study addresses such issues, with a large dataset representing the Swiss inpatient population, it provides population-based comorbidity adjustments for the prediction of mortality or other health outcomes. The slightly improved performance of the population-based weights system suggests that it might be worthwhile to derive country- or region-specific comorbidity weights from representative patient populations.
C-statistics and ROCs are widely used to assess predictive performance. Nonetheless, one downside of comparing c-statistic and ROCs is that differences between c-statistics are often small,  as it was the case when we compared our new weights and van Walraven’s. Over the past decade, it has become common to use NRIs to compare different models’ performance, even though it might differ with the cut-offs taken for analysis [44, 37]. In our study, taking the same cut-offs, NRI calculations confirmed the three weighting systems’ rankings i.e., population-based, van Walraven and Charlson weightings.
The primary strength of this study was the large sample size and the heterogeneity of the Swiss inpatient population across all general hospitals over six years, which made it representative of the entire country. To our knowledge, this study is the first to derive and validate Elixhauser weightings in Swiss hospital inpatient data. We used standard regression methodology for large datasets, including random effects at the hospital level, and internally validated our models. We also used accepted methods to modify our adjusted model into a population-based weightings system that re-includes the association of several comorbidities formerly excluded from the Elixhauser index . Despite differences in individual comorbidities’ prevalence and weightings, Charlson, Elixhauser/VW, and the population-based weights performed well across the derivation, validation, and all-cases groups. We also used NRIs, allowing a robust comparison of model performance. Finally, the methods we applied were explicit and can be replicated by other researchers, who can adjust or control for patient comorbidity via their own and national databases.
Our study also has certain notable limitations. We derived our weights using statistical criteria, while clinical knowledge might be needed to determine each comorbidity's value. Since we used codes assigned in routine data, the capture of the comorbidities could be influenced by other factors, such as physician and nurse documentation, code assignment accuracy, and the possibility that capture of comorbidities is biased towards those for which the Swiss DRG / MDC pays more [45, 41]. Additionally, as Swiss data protection regulations prevented us from obtaining the inpatients’ exact age, we could not differentiate children exactly under 18 years and could not specify each year. This also might have influenced the predictive accuracy of the tested models.