Prediction models for clustered data: comparison of a random intercept and standard regression model

BACKGROUND: When study data are clustered, standard regression analysis is considered inappropriate and analytical techniques for clustered data need to be used. For prediction research in which the interest of predictor effects is on the patient level, random effect regression models are probably preferred over standard regression analysis. It is well known that the random effect parameter estimates and the standard logistic regression parameter estimates are different. Here, we compared random effect and standard logistic regression models for their ability to provide accurate predictions. METHODS: Using an empirical study on 1642 surgical patients at risk of postoperative nausea and vomiting, who were treated by one of 19 anesthesiologists (clusters), we developed prognostic models either with standard or random intercept logistic regression. External validity of these models was assessed in new patients from other anesthesiologists. We supported our results with simulation studies using intra-class correlation coefficients (ICC) of 5%, 15%, or 30%. Standard performance measures and measures adapted for the clustered data structure were estimated. RESULTS: The model developed with random effect analysis showed better discrimination than the standard approach, if the cluster effects were used for risk prediction (standard c-index of 0.69 versus 0.66). In the external validation set, both models showed similar discrimination (standard c-index 0.68 versus 0.67). The simulation study confirmed these results. For datasets with a high ICC (≥15%), model calibration was only adequate in external subjects, if the used performance measure assumed the same data structure as the model development method: standard calibration measures showed good calibration for the standard developed model, calibration measures adapting the clustered data structure showed good calibration for the prediction model with random intercept. CONCLUSION: The models with random intercept discriminate better than the standard model only if the cluster effect is used for predictions. The prediction model with random intercept had good calibration within clusters

Patient and anesthesia characteristics of children with low pre-incision blood pressure: A retrospective observational study

Background: Intraoperative blood pressure has been suggested as a key factor for safe pediatric anesthesia. However, there is not much insight into factors that discriminate between children with low and normal pre-incision blood pressure. Our aim was to explore whether children who have a low blood pressure during anesthesia are different than those with normal blood pressure. The focus of the present study was on the pre-incision period. Methods: This retrospective study included pediatric patients undergoing anesthesia for non-cardiac surgery at a tertiary pediatric university hospital, between 2012 and 2016. We analyzed the association between pre-incision blood pressure and patient- and anesthesia characteristics, comparing low with normal pre-incision blood pressure. This association was further explored with a multivariable linear regression. Results: In total, 20 962 anesthetic cases were included. Pre-incision blood pressure was associated with age (beta −0.04 SD per year), gender (female −0.11), previous surgery (−0.15), preoperative blood pressure (+0.01 per mm Hg), epilepsy (0.12), bronchial hyperactivity (−0.18), emergency surgery (0.10), loco-regional technique (−0.48), artificial airway device (supraglottic airway device instead of tube 0.07), and sevoflurane concentration (0.03 per sevoflurane %). Conclusions: Children with low pre-incision blood pressure do not differ on clinically relevant factors from children with normal blood pressure. Although the present explorative study shows that pre-incision blood pressure is partly dependent on patient characteristics and partly dependent on anesthetic technique, other unmeasured variables might play a more important role

Barriers and facilitators perceived by physicians when using prediction models in practice

Objectives Prediction models may facilitate risk-based management of health care conditions. In a large cluster-randomized trial, presenting calculated risks of postoperative nausea and vomiting (PONV) to physicians (assistive approach) increased risk-based management of PONV. This increase did not improve patient outcome - that is, PONV incidence. This prompted us to explore how prediction tools guide the decision-making process of physicians. Study Design and Setting Using mixed methods, we interviewed eight physicians to understand how predicted risks were perceived by the physicians and how they influenced decision making. Subsequently, all 57 physicians of the trial were surveyed for how the presented risks influenced their perceptions. Results Although the prediction tool made physicians more aware of PONV prevention, the physicians reported three barriers to use predicted risks in their decision making. PONV was not considered an outcome of utmost importance; decision making on PONV prophylaxis was mostly intuitive rather than risk based; prediction models do not weigh benefits and risks of prophylactic drugs. Conclusion Combining probabilistic output of the model with their clinical experience may be difficult for physicians, especially when their decision-making process is mostly intuitive. Adding recommendations to predicted risks (directive approach) was considered an important step to facilitate the uptake of a prediction tool

An observational study of end-tidal carbon dioxide trends in general anesthesia

PURPOSE: Despite growing evidence supporting the potential benefits of higher end-tidal carbon dioxide (ETCO METHODS: This retrospective, observational, multicentre study included 317,445 adult patients who received general anesthesia for non-cardiothoracic procedures between January 2008 and September 2016. The primary outcome was a time-weighted average area-under-the-curve (TWA-AUC) for four ETCO RESULTS: Both TWA-AUC and median ETCO CONCLUSIONS: Between 2008 and 2016, intraoperative ETC

Accounting for Breakout in Britain: The Industrial Revolution Through a Malthusian Lens

Over the past few years non-cardiac surgery has been recognised as a serious circulatory stress test which may trigger cardiovascular events such as myocardial infarction, in particular in patients at high risk. Detection of these postoperative cardiovascular events is difficult as clinical symptoms often go unnoticed. To improve detection, guidelines advise to perform routine postoperative assessment of cardiac troponin. Troponin elevation – or postoperative myocardial injury – can be caused by myocardial infarction. However, also non-coronary causes, such as cardiac arrhythmias, sepsis and pulmonary embolism, may play a role in a considerable number of patients with postoperative myocardial injury. It is crucial to acquire more knowledge about the underlying mechanisms of postoperative myocardial injury because effective prevention and treatment options are lacking. Preoperative administration of beta-blockers, aspirin, statins, clonidine, angiotensin-converting enzyme inhibitors and angiotensin receptor blockers, and preoperative revascularisation have all been investigated as preventive options. Of these, only statins should be considered as the initiation or reload of statins may reduce the risk of postoperative myocardial injury. There is also not enough evidence for intraoperative measures such blood pressure optimisation or intensified medical therapy once patients have developed postoperative myocardial injury. Given the impact, better preoperative identification of patients at risk of postoperative myocardial injury, for example using preoperatively measured biomarkers, would be helpful to improve cardiac optimisation

Welke perioperatieve bètablokker heeft de voorkeur?

Guidelines on perioperative cardiovascular evaluation and management of patients undergoing non-cardiac surgery recommend initiation of beta-blocker therapy in at-risk patients who are undergoing intermediate- to high-risk surgery. Continuation of therapy in patients already receiving beta-blockers is also recommended. Recent literature, however, reported an increased risk of perioperative cardiovascular mortality among patients who continued with existing beta-blockade; most patients in this study were using metoprolol. There are important pharmacodynamic and pharmacokinetic differences between various beta-blockers, and these differences may explain the differences in clinical effects. Metoprolol has less beta1 receptor affinity compared with atenolol and bisoprolol, and beta1 receptor polymorphisms affect the clinical effects of metoprolol. Furthermore, metoprolol is dependent on activity of the CYP2D6 liver enzyme, which results in clinically important differences in plasma concentration. It is, therefore, wise to follow the European guidelines and to initiate beta-blocker therapy in the perioperative period with either atenolol or bisoprolol