Preoperative anaemia and outcome after elective cardiac surgery: a Dutch national registry analysis.

BACKGROUND: Previous studies have shown that preoperative anaemia in patients undergoing cardiac surgery is associated with adverse outcomes. However, most of these studies were retrospective, had a relatively small sample size, and were from a single centre. The aim of this study was to analyse the relationship between the severity of preoperative anaemia and short- and long-term mortality and morbidity in a large multicentre national cohort of patients undergoing cardiac surgery. METHODS: A nationwide, prospective, multicentre registry (Netherlands Heart Registration) of patients undergoing elective cardiac surgery between January 2013 and January 2019 was used for this observational study. Anaemia was defined according to the WHO criteria, and the main study endpoint was 120-day mortality. The association was investigated using multivariable logistic regression analysis. RESULTS: In total, 35 484 patients were studied, of whom 6802 (19.2%) were anaemic. Preoperative anaemia was associated with an increased risk of 120-day mortality (adjusted odds ratio [aOR] 1.7; 95% confidence interval [CI]: 1.4-1.9; P<0.001). The risk of 120-day mortality increased with anaemia severity (mild anaemia aOR 1.6; 95% CI: 1.3-1.9; P<0.001; and moderate-to-severe anaemia aOR 1.8; 95% CI: 1.4-2.4; P<0.001). Preoperative anaemia was associated with red blood cell transfusion and postoperative morbidity, the causes of which included renal failure, pneumonia, and myocardial infarction. CONCLUSIONS: Preoperative anaemia was associated with mortality and morbidity after cardiac surgery. The risk of adverse outcomes increased with anaemia severity. Preoperative anaemia is a potential target for treatment to improve postoperative outcomes.

The effect of requesting a reason for non-adherence to a guideline in a long running automated reminder system for PONV prophylaxis.

BACKGROUND: Automated reminders are employed frequently to improve guideline adherence, but limitations of automated reminders are becoming more apparent. We studied the reasons for non-adherence in the setting of automated reminders to test the hypothesis that a separate request for a reason in itself may further improve guideline adherence. METHODS: In a previously implemented automated reminder system on prophylaxis for postoperative nausea and vomiting (PONV), we included additional automated reminders requesting a reason for non-adherence. We recorded these reasons in the pre-operative screening clinic, the OR and the PACU. We compared adherence to our PONV guideline in two study groups with a historical control group. RESULTS: Guideline adherence on prescribing and administering PONV prophylaxis (dexamethasone and granisetron) all improved compared to the historical control group (89 vs. 82% (p< 0.0001), 96 vs 95% (not significant) and 90 vs 82% (p<0.0001)) while decreasing unwarranted prescription for PONV prophylaxis (10 vs. 13 %). In the pre-operative screening clinic, the main reason for not prescribing PONV prophylaxis was disagreement with the risk estimate by the decision support system. In the OR/PACU, the main reasons for not administering PONV prophylaxis were: 'unintended non-adherence' and 'failure to document'. CONCLUSIONS: In this study requesting a reason for non-adherence is associated with improved guideline adherence. The effect seems to depend on the underlying reason for non-adherence. It also illustrates the importance of human factors principles in the design of decision support. Some reasons for non-adherence may not be influenced by automated reminders.

Automated reminders increase adherence to guidelines for administration of prophylaxis for postoperative nausea and vomiting.

BACKGROUND AND OBJECTIVE: Correct identification of patients at high risk for postoperative nausea and vomiting (PONV), prescription of PONV prophylaxis and correct administration of medication are all important for effective PONV prophylaxis. This has been acknowledged by development of guidelines throughout the world. We studied the effect of introducing patient-specific automated reminders on timely administration of PONV prophylaxis medication during general anaesthesia. METHODS: During the visit to the preoperative screening clinic, patients at high risk for PONV were identified and PONV prophylaxis was prescribed. To study the effect of patient-specific decision support [a pop-up window reminding the (nurse) anaesthetist that PONV prophylaxis had been prescribed for this particular patient] on the timely administration of PONV medication, we queried our database to extract data on all patients for three consecutive periods: 6 weeks before decision support (control), 12 weeks during decision support and 6 weeks after discontinuation of decision support (postdecision support) and studied how often PONV prophylaxis was administered correctly. RESULTS: Between November 2005 and May 2006, 1727, 2594 and 1331 patients presented for elective surgery in the control, decision support and postdecision support periods, respectively. In the control period, 236 patients receiving general anaesthesia were scheduled to receive PONV prophylaxis. Of these, 93 (39%) received both dexamethasone and granisetron in the correct timeframe. This increased to 464 (79%) out of 591 patients in the decision support period and decreased back to 99 (41%) out of 243 patients in the postdecision support period (P < 0.001). CONCLUSION: Decision support is effective in improving administration and timing of PONV prophylaxis medication. After withdrawal of decision support, adherence decreased to predecision support levels.

Decision support increases guideline adherence for prescribing postoperative nausea and vomiting prophylaxis.

BACKGROUND: Guidelines for postoperative nausea and vomiting (PONV) prevention are implemented widely but their effectiveness may be limited by poor adherence. We hypothesized that the use of an electronic decision support (DS) system would significantly improve guideline adherence. METHODS: Medical information of all patients undergoing elective surgery in our regional teaching hospital is routinely entered in an anesthesia information management system at the preoperative screening clinic. Our departmental PONV prevention guidelines identifies patients as "high-risk" and thus eligible for PONV prophylaxis based on the presence of at least three of the following risk factors: female gender, history of PONV or motion sickness, nonsmoker status, and anticipated use of postoperative opioids. Using automated reminders, we studied the effect of DS on guidelines adherence using an off-on-off design. In these three study periods, we queried for all consecutive patients visiting the preoperative screening clinic who were eligible for PONV prophylaxis and studied how often it was prescribed correctly. RESULTS: Between November 2005 and June 2006, 1340, 2715, and 1035 patients were included in the control, DS and post-DS periods, respectively. As a result of mandatory data entry of risk factors, the percentage of high-risk PONV patients increased from 28% in the control period to 32% and 31% in the DS and post-DS periods, respectively. During the control period, 38% of all high-risk patients were prescribed PONV prophylaxis. This increased to 73% during the DS period and decreased to 37% in the post-DS period. CONCLUSION: Electronic DS increases guidelines adherence for the prescription of PONV prophylaxis in high-risk PONV patients.

Predictive models for thoracic aorta surgery. Is the Euroscore the optimal risk model in the Netherlands?

Prediction models do not optimally perform in the case of aorta surgery. We tried to define models that predict intensive care death for patients who underwent thoracic aorta surgery in the Netherlands. Therefore, we used data of 1290 patients who underwent interventions on the thoracic aorta from 1997 to 2002 which were prospectively collected in seven centers. One outcome was examined: intensive care death. Predicting models were made by multiple logistic regression analysis. The area under the receiver operating characteristics curve was used to study the discriminatory abilities of these models. We compared the models with the Euroscore. Eleven percent of the patients died during operation or on intensive care. Age, creatinine level >/=150 mumol/l, poor left ventricular ejection fraction and urgent indication were most related with intensive care-death. Prolonged extracorporal circulation and deep hypothermia were also of importance in the peri-operative model. The models performed better than the Euroscore. We conclude that the developed models perform relatively well in discriminating patients with respect to intensive care-death and even better than the Euroscore.