OBJECTIVES:We describe the importance of interprofessional care in modern critical care medicine. This review highlights the essential roles played by specific members of the interprofessional care ...team, including patients and family members, and discusses quality improvement initiatives that require interprofessional collaboration for success.
DATA SOURCES:Studies were identified through MEDLINE search using a variety of search phrases related to interprofessional care, critical care provider types, and quality improvement initiatives. Additional articles were identified through a review of the reference lists of identified articles.
STUDY SELECTION:Original articles, review articles, and systematic reviews were considered.
DATA EXTRACTION:Manuscripts were selected for inclusion based on expert opinion of well-designed or key studies and review articles.
DATA SYNTHESIS:“Interprofessional care” refers to care provided by a team of healthcare professionals with overlapping expertise and an appreciation for the unique contribution of other team members as partners in achieving a common goal. A robust body of data supports improvement in patient-level outcomes when care is provided by an interprofessional team. Critical care nurses, advanced practice providers, pharmacists, respiratory care practitioners, rehabilitation specialists, dieticians, social workers, case managers, spiritual care providers, intensivists, and nonintensivist physicians each provide unique expertise and perspectives to patient care, and therefore play an important role in a team that must address the diverse needs of patients and families in the ICU. Engaging patients and families as partners in their healthcare is also critical. Many important ICU quality improvement initiatives require an interprofessional approach, including Awakening and Breathing Coordination, Delirium, Early Exercise/Mobility, and Family Empowerment bundle implementation, interprofessional rounding practices, unit-based quality improvement initiatives, Patient and Family Advisory Councils, end-of-life care, coordinated sedation awakening and spontaneous breathing trials, intrahospital transport, and transitions of care.
CONCLUSIONS:A robust body of evidence supports an interprofessional approach as a key component in the provision of high-quality critical care to patients of increasing complexity and with increasingly diverse needs.
Understanding geriatric physiology is critical for successful perioperative management of older surgical patients. The frailty syndrome is evolving as an important, potentially modifiable process ...capturing a patient's biologic age and is more predictive of adverse perioperative outcomes than chronologic age. Use of frailty in risk stratification and perioperative decision-making allows providers to effectively diagnose, risk stratify, and treat patients in the perioperative setting. Further study is needed to develop a universal definition of frailty, to identify comprehensive yet feasible screening tools that allow for accurate detection of frailty in the perioperative setting, and to refine treatment programs for frail surgical patients.
Accurate, pragmatic risk stratification for postoperative delirium (POD) is necessary to target preventative resources toward high-risk patients. Machine learning (ML) offers a novel approach to ...leveraging electronic health record (EHR) data for POD prediction. We sought to develop and internally validate a ML-derived POD risk prediction model using preoperative risk features, and to compare its performance to models developed with traditional logistic regression.
This was a retrospective analysis of preoperative EHR data from 24,885 adults undergoing a procedure requiring anesthesia care, recovering in the main post-anesthesia care unit, and staying in the hospital at least overnight between December 2016 and December 2019 at either of two hospitals in a tertiary care health system. One hundred fifteen preoperative risk features including demographics, comorbidities, nursing assessments, surgery type, and other preoperative EHR data were used to predict postoperative delirium (POD), defined as any instance of Nursing Delirium Screening Scale ≥2 or positive Confusion Assessment Method for the Intensive Care Unit within the first 7 postoperative days. Two ML models (Neural Network and XGBoost), two traditional logistic regression models ("clinician-guided" and "ML hybrid"), and a previously described delirium risk stratification tool (AWOL-S) were evaluated using the area under the receiver operating characteristic curve (AUC-ROC), sensitivity, specificity, positive likelihood ratio, and positive predictive value. Model calibration was assessed with a calibration curve. Patients with no POD assessments charted or at least 20% of input variables missing were excluded.
POD incidence was 5.3%. The AUC-ROC for Neural Net was 0.841 95% CI 0. 816-0.863 and for XGBoost was 0.851 95% CI 0.827-0.874, which was significantly better than the clinician-guided (AUC-ROC 0.763 0.734-0.793, p < 0.001) and ML hybrid (AUC-ROC 0.824 0.800-0.849, p < 0.001) regression models and AWOL-S (AUC-ROC 0.762 95% CI 0.713-0.812, p < 0.001). Neural Net, XGBoost, and ML hybrid models demonstrated excellent calibration, while calibration of the clinician-guided and AWOL-S models was moderate; they tended to overestimate delirium risk in those already at highest risk.
Using pragmatically collected EHR data, two ML models predicted POD in a broad perioperative population with high discrimination. Optimal application of the models would provide automated, real-time delirium risk stratification to improve perioperative management of surgical patients at risk for POD.
BACKGROUND:Postoperative delirium is an important problem for surgical inpatients and was the target of a multidisciplinary quality improvement project at our institution. We developed and tested a ...semiautomated delirium risk stratification instrument, Age, WORLD backwards, Orientation, iLlness severity, Surgery-specific risk (AWOL-S), in 3 independent cohorts from our tertiary care hospital and describe its performance characteristics and impact on clinical care.
METHODS:The risk stratification instrument was derived with elective surgical patients who were admitted at least overnight and received at least 1 postoperative delirium screen (Nursing Delirium Screening Scale NuDESC or Confusion Assessment Method for the Intensive Care Unit CAM-ICU) and preoperative cognitive screening tests (orientation to place and ability to spell WORLD backward). Using data pragmatically collected between December 7, 2016, and June 15, 2017, we derived a logistic regression model predicting probability of delirium in the first 7 postoperative hospital days. A priori predictors included age, cognitive screening, illness severity or American Society of Anesthesiologists physical status, and surgical delirium risk. We applied model odds ratios to 2 subsequent cohorts (“validation” and “sustained performance”) and assessed performance using area under the receiver operator characteristic curves (AUC-ROC). A post hoc sensitivity analysis assessed performance in emergency and preadmitted patients. Finally, we retrospectively evaluated the use of benzodiazepines and anticholinergic medications in patients who screened at high risk for delirium.
RESULTS:The logistic regression model used to derive odds ratios for the risk prediction tool included 2091 patients. Model AUC-ROC was 0.71 (0.67–0.75), compared with 0.65 (0.58–0.72) in the validation (n = 908) and 0.75 (0.71–0.78) in the sustained performance (n = 3168) cohorts. Sensitivity was approximately 75% in the derivation and sustained performance cohorts; specificity was approximately 59%. The AUC-ROC for emergency and preadmitted patients was 0.71 (0.67–0.75; n = 1301). After AWOL-S was implemented clinically, patients at high risk for delirium (n = 3630) had 21% (3%–36%) lower relative risk of receiving an anticholinergic medication perioperatively after controlling for secular trends.
CONCLUSIONS:The AWOL-S delirium risk stratification tool has moderate accuracy for delirium prediction in a cohort of elective surgical patients, and performance is largely unchanged in emergent/preadmitted surgical patients. Using AWOL-S risk stratification as a part of a multidisciplinary delirium reduction intervention was associated with significantly lower rates of perioperative anticholinergic but not benzodiazepine, medications in those at high risk for delirium. AWOL-S offers a feasible starting point for electronic medical record–based postoperative delirium risk stratification and may serve as a useful paradigm for other institutions.
BACKGROUND:Postoperative delirium is a common and serious problem for older adults. To better align local practices with delirium prevention consensus guidelines, we implemented a 5-component ...intervention followed by a quality improvement (QI) project at our institution.
METHODS:This hybrid implementation-effectiveness study took place at 2 adult hospitals within a tertiary care academic health care system. We implemented a 5-component interventionpreoperative delirium risk stratification, multidisciplinary education, written memory aids, delirium prevention postanesthesia care unit (PACU) orderset, and electronic health record enhancements between December 1, 2017 and June 30, 2018. This was followed by a department-wide QI project to increase uptake of the intervention from July 1, 2018 to June 30, 2019. We tracked process outcomes during the QI period, including frequency of preoperative delirium risk screening, percentage of “high-risk” screens, and frequency of appropriate PACU orderset use. We measured practice change after the interventions using interrupted time series analysis of perioperative medication prescribing practices during baseline (December 1, 2016 to November 30, 2017), intervention (December 1, 2017 to June 30, 2018), and QI (July 1, 2018 to June 30, 2019) periods. Participants were consecutive older patients (≥65 years of age) who underwent surgery during the above timeframes and received care in the PACU, compared to a concurrent control group <65 years of age. The a priori primary outcome was a composite of perioperative American Geriatrics Society Beers Criteria for Potentially Inappropriate Medication Use (Beers PIM) medications. The secondary outcome, delirium incidence, was measured in the subset of older patients who were admitted to the hospital for at least 1 night.
RESULTS:During the 12-month QI period, preoperative delirium risk stratification improved from 67% (714 of 1068 patients) in month 1 to 83% in month 12 (776 of 931 patients). Forty percent of patients were stratified as “high risk” during the 12-month period (4246 of 10,494 patients). Appropriate PACU orderset use in high-risk patients increased from 19% in month 1 to 85% in month 12. We analyzed medication use in 7212, 4416, and 8311 PACU care episodes during the baseline, intervention, and QI periods, respectively. Beers PIM administration decreased from 33% to 27% to 23% during the 3 time periods, with adjusted odds ratio (aOR) 0.97 (95% confidence interval CI, 0.95–0.998; P = .03) per month during the QI period in comparison to baseline. Delirium incidence was 7.5%, 9.2%, and 8.5% during the 3 time periods with aOR of delirium of 0.98 (95% CI, 0.91–1.05, P = .52) per month during the QI period in comparison to baseline.
CONCLUSIONS:A perioperative delirium prevention intervention was associated with reduced administration of Beers PIMs to older adults.
BACKGROUND:Data suggest that pregnant women are not at elevated risk of acquiring severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection or developing severe disease compared with ...nonpregnant patients. However, management of pregnant patients who are critically ill with coronavirus disease 2019 (COVID-19) infection is complicated by physiologic changes and other pregnancy considerations and requires balancing maternal and fetal well-being.
CASE:We report the case of a patient at 28 weeks of gestation with acute respiratory distress syndrome (ARDS) from COVID-19 infection, whose deteriorating respiratory condition prompted delivery. Our patientʼs oxygenation and respiratory mechanics improved within hours of delivery, though she required prolonged mechanical ventilation until postpartum day 10. Neonatal swabs for SARS-CoV-2 and COVID-19 immunoglobulin (Ig) G and IgM were negative.
CONCLUSION:We describe our multidisciplinary management of a preterm pregnant patient with ARDS from COVID-19 infection and her neonate.
The current article reviews the importance of postoperative delirium (POD), focusing on the older surgical population, and summarizes the best-practice guidelines about POD prevention and treatment ...which have been published within the last several years. We also describe our local experience with implementing a perioperative delirium risk stratification and prevention pathway, and review implementation science principles which others may find useful as they move toward risk stratification and prevention in their own institutions.
There are few areas of consensus, backed by strong experimental data, in POD best-practice guidelines. Most guidelines recommend preoperative cognitive screening, nonpharmacologic delirium prevention measures, and avoidance of deliriogenic medications. The field of implementation science offers strategies for closing the evidence-practice gap, which we supplement with lessons learned from our own experience implementing a perioperative delirium risk stratification and prevention pathway.
POD continues to be a serious perioperative complication commonly experienced by older adults. Growing appreciation of its prognostic implications and evidence behind multidisciplinary, collaborative, and focused prevention strategies rooted in implementation science have prompted several major groups to issue consensus guidelines. Adopting best practices POD risk stratification and prevention pathways will improve perioperative care for older adults.
It is unclear whether machine learning methods yield more accurate electronic health record (EHR) prediction models compared with traditional regression methods.
The objective of this study was to ...compare machine learning and traditional regression models for 10-year mortality prediction using EHR data.
This was a cohort study.
Veterans Affairs (VA) EHR data.
Veterans age above 50 with a primary care visit in 2005, divided into separate training and testing cohorts (n= 124,360 each).
The primary outcome was 10-year all-cause mortality. We considered 924 potential predictors across a wide range of EHR data elements including demographics (3), vital signs (9), medication classes (399), disease diagnoses (293), laboratory results (71), and health care utilization (149). We compared discrimination (c-statistics), calibration metrics, and diagnostic test characteristics (sensitivity, specificity, and positive and negative predictive values) of machine learning and regression models.
Our cohort mean age (SD) was 68.2 (10.5), 93.9% were male; 39.4% died within 10 years. Models yielded testing cohort c-statistics between 0.827 and 0.837. Utilizing all 924 predictors, the Gradient Boosting model yielded the highest c-statistic 0.837, 95% confidence interval (CI): 0.835-0.839. The full (unselected) logistic regression model had the highest c-statistic of regression models (0.833, 95% CI: 0.830-0.835) but showed evidence of overfitting. The discrimination of the stepwise selection logistic model (101 predictors) was similar (0.832, 95% CI: 0.830-0.834) with minimal overfitting. All models were well-calibrated and had similar diagnostic test characteristics.
Our results should be confirmed in non-VA EHRs.
The differences in c-statistic between the best machine learning model (924-predictor Gradient Boosting) and 101-predictor stepwise logistic models for 10-year mortality prediction were modest, suggesting stepwise regression methods continue to be a reasonable method for VA EHR mortality prediction model development.
Although inhaled therapy reduces exacerbations among patients with COPD, the effectiveness of providing inhaled treatment per risk stratification models remains unclear.
Are inhaled regimens that ...align with the 2017 Global Initiative for Chronic Obstructive Lung Disease (GOLD) strategy associated with clinically important outcomes?
We conducted secondary analyses of Long-term Oxygen Treatment Trial (LOTT) data. The trial enrolled patients with COPD with moderate resting or exertional hypoxemia between 2009 and 2015. Our exposure was the patient-reported inhaled regimen at enrollment, categorized as either aligning with, undertreating, or potentially overtreating per the 2017 GOLD strategy. Our primary composite outcome was time to death or first hospitalization for COPD. Additional outcomes included individual components of the composite outcome and time to first exacerbation. We generated multivariable Cox proportional hazard models across strata of GOLD-predicted exacerbation risk (high vs low) to estimate between-group hazard ratios for time to event outcomes. We adjusted models a priori for potential confounders, clustered by site.
The trial enrolled 738 patients (73.4% men; mean age, 68.8 years). Of the patients, 571 (77.4%) were low risk for future exacerbations. Of the patients, 233 (31.6%) reported regimens aligning with GOLD recommendations; most regimens (54.1%) potentially overtreated. During a 2.3-year median follow-up, 332 patients (44.9%) experienced the composite outcome. We found no difference in time to composite outcome or death among patients reporting regimens aligning with recommendations compared with undertreated patients. Among patients at low risk, potential overtreatment was associated with higher exacerbation risk (hazard ratio, 1.42; 95% CI, 1.09-1.87), whereas inhaled corticosteroid treatment was associated with 64% higher risk of pneumonia (incidence rate ratio, 1.64; 95% CI, 1.01-2.66).
Among patients with COPD with moderate hypoxemia, we found no difference in clinical outcomes between inhaled regimens aligning with the 2017 GOLD strategy compared with those that were undertreated. These findings suggest the need to reevaluate the effectiveness of risk stratification model-based inhaled treatment strategies.
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