Genomics Testing and Personalized Medicine in the Preoperative Setting

Derivation and Validation of an Ensemble Model for the Prediction of Agitation in Mechanically Ventilated Patients Maintained Under Light Sedation

OBJECTIVES

Light sedation is recommended over deep sedation for invasive mechanical ventilation to improve clinical outcome but may increase the risk of agitation. This study aimed to develop and prospectively validate an ensemble machine learning model for the prediction of agitation on a daily basis.

DESIGN

Variables collected in the early morning were used to develop an ensemble model by aggregating four machine learning algorithms including support vector machines, C5.0, adaptive boosting with classification trees, and extreme gradient boosting with classification trees, to predict the occurrence of agitation in the subsequent 24 hours.

SETTING

The training dataset was prospectively collected in 95 ICUs from 80 Chinese hospitals on May 11, 2016, and the validation dataset was collected in 20 out of these 95 ICUs on December 16, 2019.

PATIENTS

Invasive mechanical ventilation patients who were maintained under light sedation for 24 hours prior to the study day and who were to be maintained at the same sedation level for the next 24 hours.

INTERVENTIONS

None.

MEASUREMENTS AND MAIN RESULTS

A total of 578 invasive mechanical ventilation patients from 95 ICUs in 80 Chinese hospitals, including 459 in the training dataset and 119 in the validation dataset, were enrolled. Agitation was observed in 36% (270/578) of the invasive mechanical ventilation patients. The stepwise regression model showed that higher body temperature (odds ratio for 1°C increase: 5.29; 95% CI, 3.70-7.84; p < 0.001), greater minute ventilation (odds ratio for 1 L/min increase: 1.15; 95% CI, 1.02-1.30; p = 0.019), higher Richmond Agitation-Sedation Scale (odds ratio for 1-point increase: 2.43; 95% CI, 1.92-3.16; p < 0.001), and days on invasive mechanical ventilation (odds ratio for 1-d increase: 0.95; 95% CI, 0.93-0.98; p = 0.001) were independently associated with agitation in the subsequent 24 hours. In the validation dataset, the ensemble model showed good discrimination (area under the receiver operating characteristic curve, 0.918; 95% CI, 0.866-0.969) and calibration (Hosmer-Lemeshow test p = 0.459) in predicting the occurrence of agitation within 24 hours.

CONCLUSIONS

This study developed an ensemble model for the prediction of agitation in invasive mechanical ventilation patients under light sedation. The model showed good calibration and discrimination in an independent dataset.

Genomics testing and personalized medicine in the preoperative setting: Can it change outcomes in postoperative pain management?

Postoperative pain and opioid use are major challenges in perioperative medicine. Pain perception and its response to opioid use are multi-faceted and include pharmacological, psychological, and genetic components. Precision medicine is a unique approach to individualized health care in which decisions in management are based on genetics, lifestyle, and environment of each person. Genetic variations can have an impact on the perception of pain and response to treatment. This can have an effect on pain management in both acute and chronic settings. Although there is currently not enough evidence for making recommendations about genetic testing to guide pain management in the acute care setting, there are some known polymorphisms that play a role in surgical pain and opioid-related postoperative adverse outcomes. In this review, we describe the potential use of pharmacogenomics (PGx) for improving perioperative pain management. We first review a number of genotypes that have shown correlations with pain and opioid use and then describe the importance of PGx-guided analgesic protocols and implementation of screening in a preoperative evaluation clinical setting.

Perioperative Precision Medicine: Where Are We in 2020?

PURPOSE OF REVIEW

The present review examines how targeted approaches to care, based on individual variability in patient characteristics, could be applied in the perioperative setting. Such an approach would enhance individualized risk assessment and allow for targeted preventive and therapeutic decision-making in patients at increased risk for adverse perioperative events.

RECENT FINDINGS

Prior and current studies highlight valuable lessons on how future investigations attempting to link specific patient-related characteristics or treatment modalities with outcomes and adverse drug responses might be designed in the perioperative setting.

SUMMARY

Our review highlights the past, present, and future directions of perioperative precision medicine. Current evidence provides important lessons on how a specific patient and disease tailored approach can help perioperative physicians in delivering the most appropriate and safest perioperative care.