RESUMO
BACKGROUND: Multimorbidity is a growing burden in our ageing society and is associated with perioperative morbidity and mortality. Despite several modifications to the ASA physical status classification, multimorbidity as such is still not considered. Thus, the aim of this study was to quantify the burden of comorbidities in perioperative patients and to assess, independent of ASA class, its potential influence on perioperative outcome. METHODS: In a subpopulation of the prospective ClassIntra® validation study from eight international centres, type and severity of anaesthesia-relevant comorbidities were additionally extracted from electronic medical records for the current study. Patients from the validation study were of all ages, undergoing any type of in-hospital surgery and were followed up until 30 days postoperatively to assess perioperative outcomes. Primary endpoint was the number of comorbidities across ASA classes. The associated postoperative length of hospital stay (pLOS) and Comprehensive Complication Index (CCI®) were secondary endpoints. On a scale from 0 (no complication) to 100 (death) the CCI® measures the severity of postoperative morbidity as a weighted sum of all postoperative complications. RESULTS: Of 1421 enrolled patients, the mean number of comorbidities significantly increased from 1.5 in ASA I (95% CI, 1.1-1.9) to 10.5 in ASA IV (95% CI, 8.3-12.7) patients. Furthermore, independent of ASA class, postoperative complications measured by the CCI® increased per each comorbidity by 0.81 (95% CI, 0.40-1.23) and so did pLOS (geometric mean ratio, 1.03; 95% CI, 1.01-1.06). CONCLUSIONS: These data quantify the high prevalence of multimorbidity in the surgical population and show that the number of comorbidities is predictive of negative postoperative outcomes, independent of ASA class.
RESUMO
AIMS OF THE STUDY: Multimorbidity is a growing global health problem, resulting in an increased perioperative risk for surgical patients. Data on both the prevalence of multimorbidity and its impact on perioperative outcome are limited. The American Society of Anesthesiologists (ASA) classification uses only the single most severe systemic disease to define the ASA class and ignores multimorbidity. This study aimed to assess the number and type of all anaesthesia-relevant comorbidities and to analyse their impact on outcome and hospital costs. METHODS: This cohort study is nested in the ClassIntra® validation study and includes only patients enrolled at the University Hospital of Basel. Approximately 30 patients per surgical discipline undergoing any type of in-hospital surgery were followed up until hospital discharge to record all intra- and postoperative adverse events. In addition, the type and severity of all perioperatively relevant comorbidities were extracted from the electronic medical record according to a predefined list. The primary endpoint was the number of all anaesthesia-relevant comorbidities by ASA class. Using structural equation models, the direct and indirect effects of comorbidities on costs were estimated after adjustment for the ASA class and further relevant confounders and mediators. RESULTS: Of 320 enrolled patients, 27 were ASA I (8%), 150 ASA II (47%), 116 ASA III (36%) and 27 ASA IV (8%). The median number of comorbidities per patient was 5 (range 0-18), this number significantly increasing with higher ASA class: 1 comorbidity (95% CI 0.0-2.0) in ASA I, 4 comorbidities (3.8-4.2) in ASA II, 9 (8.1-9.9) in ASA III and 12 (10-14) in ASA IV patients. Independent of ASA class, each additional comorbidity increased hospital costs by EUR 1,198 (95% CI 288-2108) with almost identical proportions of direct and indirect effects. The number of anaesthesia-relevant comorbidities also increased postoperative complications and postoperative length of hospital stay. CONCLUSIONS: Multimorbidity in perioperative patients is highly prevalent and has a relevant impact on hospital costs, independent of the ASA class. Incorporating multimorbidity into the ASA classification might be warranted to improve its predictive ability and support adequate reimbursement.
