A Mobile Post Anesthesia Care Unit Order Reminder System Improves Timely Order Entry.

Transition to the postanesthesia care unit (PACU) requires timely order placement by anesthesia providers. Computerized ordering enables automated order reminder systems, but their value is not fully understood. We performed a single-center, retrospective cohort study to estimate the association between automated PACU order reminders and primary outcomes (1) on-time order placement and (2) the degree of delay in placement. As a secondary post-hoc analysis, we studied the association between late order placement and PACU outcomes. We included patients with a qualifying postprocedure order from January 1, 2019, to May 31, 2023. We excluded cases transferred directly to the ICU, whose anesthesia provider was involved in the pilot testing of the reminder system, or those with missing covariate data. Order reminder system usage was defined by the primary attending anesthesiologist's receipt of a push notification reminder on the day of surgery. We estimated the association between reminder system usage and timely order placement using a logistic regression. For patients with late orders, we performed a survival analysis of order placement. The significance level was 0.05. Patient (e.g., age, race), procedural (e.g., anesthesia duration), and provider-based (e.g., ordering privileges) variables were used as covariates within the analyses. Reminders were associated with 51% increased odds of order placement prior to PACU admission (Odds Ratio: 1.51; 95% Confidence Interval: 1.43, 1.58; p ≤ 0.001), reducing the incidence of late PACU orders from 17.5% to 12.6% (p ≤ 0.001). In patients with late orders, the reminders were associated with 10% quicker placement (Hazard Ratio: 1.10; 95% CI 1.05, 1.15; p < 0.001). On-time order placement was associated with decreased PACU duration (p < 0.001), decreased odds of peak PACU pain score (p < 0.001), and decreased odds of multiple administration of antiemetics (p = 0.02). An order reminder system was associated with an increase in order placement prior to PACU arrival and a reduction in delay in order placement after arrival.

Effectiveness of Oral Iron Therapy in Anemic Inpatient Pregnant Women: A Single Center Retrospective Cohort Study.

Background and aim Oral iron therapy is effective in treating iron deficiency anemia in outpatient pregnant women but has not been studied in inpatient pregnant women. We aimed to evaluate the effect of oral iron therapy versus no therapy during hospitalization on maternal and neonatal outcomes in women with anemia who are hospitalized for pregnancy-related morbidities (i.e., preterm premature rupture of membranes, preterm labor, pre-eclampsia, abnormal placentation, or fetal monitoring). Methods A retrospective, single-center study was conducted in hospitalized pregnant women (2018 to 2020) with inpatient stays of more than three days. The primary outcome was a change in hemoglobin level from admission to delivery in women treated with oral iron compared with those left untreated. Secondary outcomes included the total amount of iron administered before delivery, the time interval from admission to delivery, and neonatal effects. Results Two hundred sixty-three women were admitted, 79 women had anemia, and 29 (36.7%) received at least one dose of oral iron. Baseline patient characteristics were similar between groups. The median (interquartile range) dose of iron in the oral iron group was 1185.0 (477.0, 1874.0) mg. Neither absolute hemoglobin before delivery (control group: 10.0±1.2 g/dL; iron group: 10.1±1.1 g/dL; p=0.774) nor change in hemoglobin from admission to delivery (control group: -0.1±1.1 g/dL vs. iron group: 0.4±1.1 g/dL; p=0.232) differed between groups. Women in the control group had shorter length of stay (LOS) median (IQR) than women in the iron group (control group: 7.1 (5.0, 13.7) days; iron group: 11.4 (7.4, 25.9) days; p=0.03). There were no differences in maternal mode of delivery, though each group had high rates of cesarean delivery (control group: 53.7%; iron group: 72.4%; p=0.181). There were no differences in estimated blood loss at delivery (control group: 559±401; iron group: 662.1±337.4;p=0.264) in either group. Neonatal birthweight (control group: 1.9±0.7 kg; iron group: 1.9±0.7 kg; p=0.901), birth hemoglobin (control group: 16.3±2.2 g/dL; iron group: 16±2.2 g/dL; p=0.569), neonatal intensive care unit (NICU) admission (control group: 93.3%; iron group: 84.8%;p=0.272 ), or neonatal death (control group: 8.9%; iron group: 3%; p=0.394) were not different between groups. Conclusions Oral iron administered to anemic inpatient pregnant women was not associated with higher hemoglobin concentrations before delivery. Lack of standardized iron regimens and short hospital stays may contribute to the inefficacy of oral iron for this inpatient pregnant population. The small sample size and retrospective nature of this study are limiting factors in drawing conclusive evidence from this study.

Association between Preoperative Blood Pressures and Postoperative Adverse Events.

BACKGROUND: The relationship between postoperative adverse events and blood pressures in the preoperative period remains poorly understood. This study tested the hypothesis that day-of-surgery preoperative blood pressures are associated with postoperative adverse events. METHODS: The authors conducted a retrospective, observational study of adult patients having elective procedures requiring an inpatient stay between November 2017 and July 2021 at Vanderbilt University Medical Center to examine the independent associations between preoperative systolic and diastolic blood pressures (SBP, DBP) recorded immediately before anesthesia care and number of postoperative adverse events-myocardial injury, stroke, acute kidney injury, and mortality-while adjusting for potential confounders. The study used multivariable ordinal logistic regression to model the relationship. RESULTS: The analysis included 57,389 cases. The overall incidence of myocardial injury, stroke, acute kidney injury, and mortality within 30 days of surgery was 3.4% (1,967 events), 0.4% (223), 10.2% (5,871), and 2.1% (1,223), respectively. The independent associations between both SBP and DBP measurements and number of postoperative adverse events were found to be U-shaped, with greater risk both above and less than SBP 143 mmHg and DBP 86 mmHg-the troughs of the curves. The associations were strongest at SBP 173 mmHg (adjusted odds ratio, 1.212 vs. 143 mmHg; 95% CI, 1.021 to 1.439; P = 0.028), SBP 93 mmHg (adjusted odds ratio, 1.339 vs. 143 mmHg; 95% CI, 1.211 to 1.479; P < 0.001), DBP 106 mmHg (adjusted odds ratio, 1.294 vs. 86 mmHg; 95% CI, 1.003 to 1.17671; P = 0.048), and DBP 46 mmHg (adjusted odds ratio, 1.399 vs. 86 mmHg; 95% CI, 1.244 to 1.558; P < 0.001). CONCLUSIONS: Preoperative blood pressures both less than and above a specific threshold were independently associated with a higher number of postoperative adverse events, but the data do not support specific strategies for managing patients with low or high blood pressure on the day of surgery.

The impact of a medically directed student registered nurse anesthesia staffing model on Postprocedural patient outcomes.

STUDY OBJECTIVE: In 2018, the American Society of Anesthesiologists stated that student registered nurse anesthetists (SRNAs) "are not yet fully qualified anesthesia personnel." It remains unclear, however, whether postprocedural outcomes are affected by SRNAs providing anesthesia care under the medical direction of anesthesiologists, as compared with medically directed anesthesiology fellows or residents, or certified registered nurse anesthetists (CRNAs). We therefore aimed to examine whether medically directed SRNAs serving as in-room anesthesia providers impact surgical outcomes. DESIGN: Retrospective, matched-cohort analysis. SETTING: Adult patients (≥18 years old) undergoing inpatient surgery between 2000 and 2017 at a tertiary academic medical center. PATIENTS: 15,365 patients exclusively cared for by medically directed SRNAs were matched to 15,365 cared for by medically directed CRNAs, anesthesiology residents, and/or fellows. INTERVENTIONS: None. MEASUREMENTS: The primary composite outcome was postoperative occurrence of in-hospital mortality and six categories of major morbidities (infectious, bleeding, serious cardiac, gastrointestinal, respiratory, and urinary complications). In-hospital mortality was analyzed as the secondary outcome. MAIN RESULTS: In all, 30,730 cases were matched using propensity score matching to control for potential confounding. The primary outcome was identified in 2295 (7.5%) cases (7.5% with exclusive medically directed SRNAs vs 7.4% with medically directed CRNAs, residents and/or fellows; relative risk, 1.02; 95% CI, 0.94-1.11). Thus, our effort to determine noninferiority (10% difference in relative risk) with other providers was inconclusive (P = .07). However, the medically directed SRNA group (0.8% [118]) was found to be noninferior (P < .001) to the matched group (1.0% [156]) on in-hospital mortality (relative risk, 0.75; 95% CI, 0.59-0.96). CONCLUSIONS: Among 30,730 patients undergoing inpatient surgery at a single hospital, findings were inconclusive regarding whether exclusive medically directed SRNAs as in-room providers were noninferior to other providers. The use of medically directed SRNAs under this staffing model should be subject to further review. Clinical Trial and Registry URL: Not applicable.

Improving Neuromuscular Monitoring Through Education-Based Interventions and Studying Its Association With Adverse Postoperative Outcomes: A Retrospective Observational Study.

BACKGROUND: We assessed the association between education-based interventions, the frequency of train-of-four (TOF) monitoring, and postoperative outcomes. METHODS: We studied adults undergoing noncardiac surgery from February 1, 2020 through October 31, 2021. Our education-based interventions consisted of 3 phases. An interrupted time-series analysis, adjusting for patient- and procedure-related characteristics and secular trends over time, was used to assess the associations between education-based interventions and the frequency of TOF monitoring, postoperative pulmonary complications (PPCs), 90-day mortality, and sugammadex dosage. For each outcome and intervention phase, we tested whether the intervention at that phase was associated with an immediate change in the outcome or its trend (weekly rate of change) over time. In a sensitivity analysis, the association between education-based interventions and postoperative outcomes was adjusted for TOF monitoring. RESULTS: Of 19,422 cases, 11,636 (59.9%) had documented TOF monitoring. Monitoring frequency increased from 44.2% in the first week of preintervention stage to 83.4% in the final week of the postintervention phase. During the preintervention phase, the odds of TOF monitoring trended upward by 0.5% per week (odds ratio [OR], 1.005; 95% confidence interval [CI], 1.002-1.007). Phase 1 saw an immediate 54% increase (OR, 1.54; 95% CI, 1.33-1.79) in the odds, and the trend OR increased by 3% (OR, 1.03; 95% CI, 1.01-1.05) to 1.035, or 3.5% per week (joint Wald test, P < .001). Phase 2 was associated with a further immediate 29% increase (OR, 1.29; 95% CI, 1.02-1.64) but no significant association with trend (OR, 0.96; 95% CI, 0.93-1.01) of TOF monitoring (joint test, P = .04). Phase 3 and postintervention phase were not significantly associated with the frequency of TOF monitoring (joint test, P = .16 and P = .61). The study phases were not significantly associated with PPCs or sugammadex administration. The trend OR for 90-day mortality was larger by 24% (OR, 1.24; 95% CI, 1.06-1.45; joint test, P = .03) in phase 2 versus phase 1, from a weekly decrease of 8% to a weekly increase of 14%. However, this trend reversed again at the transition from phase 3 to the postintervention phase (OR, 0.82; 95% CI, 0.68-0.99; joint test, P = .05), from a 14% weekly increase to a 6.2% weekly decrease in the odds of 90-day mortality. In sensitivity analyses, adjusting for TOF monitoring, we found similar associations between study initiatives and postoperative outcomes. TOF monitoring was associated with lower odds of PPCs (OR, 0.69; 95% CI, 0.55-0.86) and 90-day mortality (OR, 0.79; 95% CI, 0.63-0.98), but not sugammadex dosing (mean difference, -0.02; 95% CI, -0.04 to 0.01). CONCLUSIONS: Our education-based interventions were associated with both TOF utilization and 90-day mortality but were not associated with either the odds of PPCs or sugammadex dosing. TOF monitoring was associated with reduced odds of PPCs and 90-day mortality.

Analysis of the factors contributing to residual weakness after sugammadex administration in pediatric patients under 2 years of age.

BACKGROUND: Sugammadex reverses the neuromuscular blockade induced by rocuronium and vecuronium and is approved by the U.S. Food and Drug Administration for use in patients aged over 2 years. There is, however, a paucity of data regarding its dosing profile in infants and children younger than 2 years. AIMS: The aim of this study was to assess the risk of recurarization, or re-paralysis, in children under 2 years of age to increase awareness on the importance of appropriate neuromuscular blocked monitoring and reversal. METHODS: All patients aged ≤24 months who underwent an operative procedure at a tertiary medical center between January 1, 2018, and December 31, 2021, and received both rocuronium for neuromuscular blockade and sugammadex for neuromuscular blockade reversal, were identified in the electronic medical record. Patients were excluded from analysis if they (1) received vecuronium, cisatracurium, atracurium, or succinylcholine for neuromuscular blockade, (2) received neostigmine for reversal, or (3) underwent more than one operation within 24 h. We performed a survival analysis of sugammadex redose using a Cox proportional hazards model. RESULTS: We reviewed 2923 records. Sugammadex was redosed in 123 (4.2%) cases. The median [IQR] time to redose was 7 [4-17] min, and the median [IQR] amount of redose administered was 2.74 [1.96-3.99] mg/kg. Increasing patient age (p < .01) and weight (p < .01) were associated with reduced hazard rate of sugammadex redose. For a patient of median weight, increasing age from 3 to 13 months was associated with a 53% risk reduction (HR: 0.47; 95% CI: 0.24-0.91). For a patient of median age, increasing weight from 4.7 to 9.2 kg was associated with 41% risk reduction (HR: 0.59; 95% CI: 0.32-1.07). We failed to detect any other associations. CONCLUSIONS: In this single-center, retrospective cohort study of pediatric surgery patients, there was an association between the hazard of sugammadex redose with both increased age and weight.

External validation of a predictive model for reintubation after cardiac surgery: A retrospective, observational study.

STUDY OBJECTIVE: Explore validation of a model to predict patients' risk of failing extubation, to help providers make informed, data-driven decisions regarding the optimal timing of extubation. DESIGN: We performed temporal, geographic, and domain validations of a model for the risk of reintubation after cardiac surgery by assessing its performance on data sets from three academic medical centers, with temporal validation using data from the institution where the model was developed. SETTING: Three academic medical centers in the United States. PATIENTS: Adult patients arriving in the cardiac intensive care unit with an endotracheal tube in place after cardiac surgery. INTERVENTIONS: Receiver operating characteristic (ROC) curves and concordance statistics were used as measures of discriminative ability, and calibration curves and Brier scores were used to assess the model's predictive ability. MEASUREMENTS: Temporal validation was performed in 1642 patients with a reintubation rate of 4.8%, with the model demonstrating strong discrimination (optimism-corrected c-statistic 0.77) and low predictive error (Brier score 0.044) but poor model precision and recall (Optimal F1 score 0.29). Combined domain and geographic validation were performed in 2041 patients with a reintubation rate of 1.5%. The model displayed solid discriminative ability (optimism-corrected c-statistic = 0.73) and low predictive error (Brier score = 0.0149) but low precision and recall (Optimal F1 score = 0.13). Geographic validation was performed in 2489 patients with a reintubation rate of 1.6%, with the model displaying good discrimination (optimism-corrected c-statistic = 0.71) and predictive error (Brier score = 0.0152) but poor precision and recall (Optimal F1 score = 0.13). MAIN RESULTS: The reintubation model displayed strong discriminative ability and low predictive error within each validation cohort. CONCLUSIONS: Future work is needed to explore how to optimize models before local implementation.

Development of a machine learning model to predict intraoperative transfusion and guide type and screen ordering.

STUDY OBJECTIVE: To develop an algorithm to predict intraoperative Red Blood Cell (RBC) transfusion from preoperative variables contained in the electronic medical record of our institution, with the goal of guiding type and screen ordering. DESIGN: Machine Learning model development on retrospective single-center hospital data. SETTING: Preoperative period and operating room. PATIENTS: The study included patients ≥18 years old who underwent surgery during 2019-2022 and excluded those who refused transfusion, underwent emergency surgery, or surgery for organ donation after cardiac or brain death. INTERVENTION: Prediction of intraoperative transfusion vs. no intraoperative transfusion. MEASUREMENTS: The outcome variable was intraoperative transfusion of RBCs. Predictive variables were surgery, surgeon, anesthesiologist, age, sex, body mass index, race or ethnicity, preoperative hemoglobin (g/dL), partial thromboplastin time (s), platelet count x 109 per liter, and prothrombin time. We compared the performances of seven machine learning algorithms. After training and optimization on the 2019-2021 dataset, model thresholds were set to the current institutional performance level of sensitivity (93%). To qualify for comparison, models had to maintain clinically relevant sensitivity (>90%) when predicting on 2022 data; overall accuracy was the comparative metric. MAIN RESULTS: Out of 100,813 cases that met study criteria from 2019 to 2021, intraoperative transfusion occurred in 5488 (5.4%) of cases. The LightGBM model was the highest performing algorithm in external temporal validity experiments, with overall accuracy of (76.1%) [95% confidence interval (CI), 75.6-76.5], while maintaining clinically relevant sensitivity of (91.2%) [95% CI, 89.8-92.5]. If type and screens were ordered based upon the LightGBM model, the predicted type and screen to transfusion ratio would improve from 8.4 to 5.1. CONCLUSIONS: Machine learning approaches are feasible in predicting intraoperative transfusion from preoperative variables and may improve preoperative type and screen ordering practices when incorporated into the electronic health record.

PeRiOperative Glucose PRAgMatic (PROGRAM) trial protocol and statistical analysis plan for comparing automated intraoperative reminders to standardise insulin administration in surgical patients at high risk of hyperglycaemia.

INTRODUCTION: Studies finding perioperative hyperglycaemia is associated with adverse patient outcomes in surgical procedures spurred the development of blood glucose guidelines at many institutions. In this trial, we will assess the implementation of a clinical decision support tool that is integrated into the intraoperative portion of our electronic health record and provides real-time best practice recommendations for intraoperative insulin dosing in surgical patients at high risk for hyperglycaemia. METHODS AND DESIGN: We will assess this intervention using a sequential and repeated cross-over design at the institutional level with periods of time for wash-out, control and study intervention. The unit of analysis will be the surgical case. The primary outcome will be the frequency of hyperglycaemia (>180 mg/dL (10 mmol/L)) at first postoperative anaesthesia care unit measurement. There are several prespecified secondary analyses focused on perioperative glycaemic control. DISCUSSION: This protocol and statistical analysis plan describes the methodology, primary and secondary analyses. The PeRiOperative Glucose PRAgMatic (PROGRAM) trial was approved by the Vanderbilt University Institutional Review Board (IRB), Vanderbilt University Medical Center, Nashville, Tennessee, USA (IRB, 220991). The study results will be disseminated via publication in a peer-reviewed journal and presented at national scientific conferences. The results of PROGRAM trial will inform best practice for perioperative standardised insulin administration in surgical patients at high risk of hyperglycaemia. TRIAL REGISTRATION NUMBER: NCT05426096.

The IMpact of PerioperAtive KeTamine on Enhanced Recovery after Abdominal Surgery (IMPAKT ERAS): protocol for a pragmatic, randomized, double-blinded, placebo-controlled trial.

BACKGROUND: Multimodal analgesic strategies that reduce perioperative opioid consumption are well-supported in Enhanced Recovery After Surgery (ERAS) literature. However, the optimal analgesic regimen has not been established, as the contributions of each individual agent to the overall analgesic efficacy with opioid reduction remains unknown. Perioperative ketamine infusions can decrease opioid consumption and opioid-related side effects. However, as opioid requirements are drastically minimized within ERAS models, the differential effects of ketamine within an ERAS pathway remain unknown. We aim to pragmatically investigate through a learning healthcare system infrastructure how the addition of a perioperative ketamine infusion to mature ERAS pathways affects functional recovery. METHODS: The IMPAKT ERAS trial (IMpact of PerioperAtive KeTamine on Enhanced Recovery after Abdominal Surgery) is a single center, pragmatic, randomized, blinded, placebo-controlled trial. 1544 patients undergoing major abdominal surgery will be randomly allocated to receive intraoperative and postoperative (up to 48 h) ketamine versus placebo infusions as part of a perioperative multimodal analgesic regimen. The primary outcome is length of stay, defined as surgical start time until hospital discharge. Secondary outcomes will include a variety of in-hospital clinical end points derived from the electronic health record. DISCUSSION: We aimed to launch a large-scale, pragmatic trial that would easily integrate into routine clinical workflow. Implementation of a modified consent process was critical to preserving our pragmatic design, permitting an efficient, low-cost model without reliance on external study personnel. Therefore, we partnered with leaders of our Investigational Review Board to develop a novel, modified consent process and shortened written consent form that would meet all standard elements of informed consent, yet also allow clinical providers the ability to recruit and enroll patients during their clinical workflow. Our trial design has created a platform for subsequent pragmatic studies at our institution. TRIAL REGISTRATION: NCT04625283, Pre-results.

Effects of Postoperative Blood Pressure Management on Delirium Among Patients in the Intensive Care Unit After Cardiac Surgery: An Observational Cohort Study.

OBJECTIVES: This study aimed to determine whether blood pressure control in the early postoperative period was associated with postoperative delirium in the cardiovascular intensive care unit (ICU). DESIGN: Observational cohort study. SETTING: Single large academic institution with a high volume of cardiac surgery. PARTICIPANTS: Cardiac surgery patients admitted to the cardiovascular ICU after surgery. INTERVENTIONS: Observational study. MEASUREMENTS AND MAIN RESULTS: A total of 517 cardiac surgery patients had mean arterial pressure (MAP) data recorded minute-by-minute for 12 postoperative hours. The time spent in each of the 7 prespecified blood pressure bands was calculated, and the development of delirium was recorded in the ICU. A multivariate Cox regression model was developed using the least absolute shrinkage and selection operator approach to identify associations between time spent in each MAP range band and delirium. Compared with the reference band of 60-to-69 mmHg, longer durations spent in 3 bands were independently associated with a lower risk of delirium: 50-to-59 mmHg band (adjusted hazard ratio [HR] 0.907 [per 10 minutes]; 95% CI 0.861-0.955); 70-to-79 mmHg band (adjusted HR 0.923 [per 10 minutes]; 95% CI 0.902-0.944); 90-to-99 mmHg band (adjusted HR 0.898 [per 10 minutes]; 95% CI 0.853-0.945). CONCLUSIONS: The MAP range bands above and below the authors' reference band of 60-to- 69 mmHg were associated with decreased risk of ICU delirium development; however, this was difficult to reconcile with a plausible biologic mechanism. Therefore, the authors did not find a correlation between early postoperative MAP control and increased risk of the development of ICU delirium after cardiac surgery.

The IMpact of PerioperAtive KeTamine on Enhanced Recovery after Abdominal Surgery (IMPAKT ERAS): protocol for a pragmatic, randomized, double-blinded, placebo-controlled trial.

BACKGROUND: Multimodal analgesic strategies that reduce perioperative opioid consumption are well-supported in Enhanced Recovery After Surgery (ERAS) literature. However, the optimal analgesic regimen has not been established, as the contributions of each individual agent to the overall analgesic efficacy with opioid reduction remains unknown. Perioperative ketamine infusions can decrease opioid consumption and opioid-related side effects. However, as opioid requirements are drastically minimized within ERAS models, the differential effects of ketamine within an ERAS pathway remain unknown. We aim to pragmatically investigate through a learning healthcare system infrastructure how the addition of a perioperative ketamine infusion to mature ERAS pathways affects functional recovery. METHODS: The IMPAKT ERAS trial (IMpact of PerioperAtive KeTamine on Enhanced Recovery after Abdominal Surgery) is a single center, pragmatic, randomized, blinded, placebo-controlled trial. 1544 patients undergoing major abdominal surgery will be randomly allocated to receive intraoperative and postoperative (up to 48 hours) ketamine versus placebo infusions as part of a perioperative multimodal analgesic regimen. The primary outcome is length of stay, defined as surgical start time until hospital discharge. Secondary outcomes will include a variety of in-hospital clinical end points derived from the electronic health record. DISCUSSION: We aimed to launch a large-scale, pragmatic trial that would easily integrate into routine clinical workflow. Implementation of a modified consent process was critical to preserving our pragmatic design, permitting an efficient, low-cost model without reliance on external study personnel. Therefore, we partnered with leaders of our Investigational Review Board to develop a novel, modified consent process and shortened written consent form that would meet all standard elements of informed consent, yet also allow clinical providers the ability to recruit and enroll patients during their clinical workflow. Our trial design has created a platform for subsequent pragmatic studies at our institution. TRIAL REGISTRATION NUMBER: NCT04625283, Pre-results Protocol Version 1.0, 2021.

The Association Between Enhanced Recovery After Cardiac Surgery-Guided Analgesics and Postoperative Delirium.

OBJECTIVES: Delirium is a common postoperative complication associated with death and long-term cognitive impairment. The authors studied the association between opioid-sparing anesthetics, incorporating Enhanced Recovery After Cardiac Surgery (ERACS)-guided analgesics and postoperative delirium. DESIGN: The authors performed a retrospective review of nonemergent coronary, valve, or ascending aorta surgery patients. SETTING: A tertiary academic medical institution. PARTICIPANTS: The study authors analyzed a dataset of elective adult cardiac surgical patients. All patients ≥18 years undergoing elective cardiac surgery from November 2, 2017 until February 2, 2021 were eligible for inclusion. INTERVENTIONS: The ERACS-guided multimodal pain regimen included preoperative oral acetaminophen and gabapentin, and intraoperative intravenous lidocaine, ketamine, and dexmedetomidine. MEASUREMENTS AND MAIN RESULTS: Delirium was measured by bedside nurses using the Confusion Assessment Method for the intensive care unit (ICU). Delirium occurred in 220 of the 1,675 patients (13.7%). The use of any component of the multimodal pain regimen was not associated with delirium (odds ratio [OR]: 0.85 [95% CI: 0.63-1.16]). Individually, acetaminophen was associated with reduced odds of delirium (OR: 0.60 [95% CI: 0.37-0.95]). Gabapentin (OR: 1.36 [95% CI: 0.97-2.21]), lidocaine (OR: 0.86 [95% CI: 0.53-1.37]), ketamine (OR: 1.15 [95% CI: 0.72-1.83]), and dexmedetomidine (OR: 0.79 [95% CI: 0.46-1.31]) were not individually associated with postoperative delirium. Individual ERACS elements were associated with secondary outcomes of hospital length of stay, ICU duration, postoperative opioid administration, and postoperative intubation duration. CONCLUSIONS: The use of an opioid-sparing perioperative ERACS pain regimen was not associated with reduced postoperative delirium, opioid consumption, or additional poor outcomes. Individually, acetaminophen was associated with reduced delirium.

Patient and Operative Factors Predict Risk of Discretionary Prolonged Postoperative Mechanical Ventilation in a Broad Surgical Cohort.

BACKGROUND: Patients undergoing surgery with general anesthesia and endotracheal intubation are ideally extubated upon case completion, as prolonged postoperative mechanical ventilation (PPMV) has been associated with poor outcomes. However, some patients require PPMV for surgical reasons, such as airway compromise, while others remain intubated at the discretion of the anesthesia provider. Incidence and risk factors for discretionary PPMV (DPPMV) have been described in individual surgical subspecialties and intensive care unit (ICU) populations, but are relatively understudied in a broad surgical cohort. The present study seeks to fill this gap and identify the perioperative risk factors that predict DPPMV. METHODS: After obtaining institutional review board (IRB) exemption, existing electronic health record databases at our large referral center were retrospectively queried for adult surgeries performed between January 2018 and December 2020 with general anesthesia, endotracheal intubation, and by surgical services that do not routinely leave patients intubated for surgical reasons. Patients who arrived to the ICU intubated after surgery were identified as experiencing DPPMV. Selection of candidate risk factors was performed with LASSO-regularized logistic regression, and surviving variables were used to generate a multivariable logistic regression model of DPPMV risk. RESULTS: A total of 32,915 cases met inclusion criteria, of which 415 (1.26%) experienced DPPMV. Compared to extubated patients, those with DPPMV were more likely to have undergone emergency surgery (42.9% versus 3.4%; P < .001), surgery during an existing ICU stay (30.8% versus 2.8%; P < 0.001), and have 20 of the 31 elixhauser comorbidities ( P < .05 for each comparison), among other differences. A risk model with 12 variables, including American Society of Anesthesiologists (ASA) physical classification status, emergency surgery designation, four Elixhauser comorbidities, surgery during an existing ICU stay, surgery duration, estimated number of intraoperative handoffs, and vasopressor, sodium bicarbonate, and albuterol administration, yielded an area under the receiver operating characteristic curve of 0.97 (95% confidence interval, 0.96-0.97) for prediction of DPPMV. CONCLUSIONS: DPPMV was uncommon in this broad surgical cohort but could be accurately predicted using readily available patient-specific and operative factors. These results may be useful for preoperative risk stratification, postoperative resource allocation, and clinical trial planning.

Association of Time to Surgery After COVID-19 Infection With Risk of Postoperative Cardiovascular Morbidity.

Importance: The time interval between COVID-19 infection and surgery is a potentially modifiable but understudied risk factor for postoperative complications. Objective: To examine the association between time to surgery after COVID-19 diagnosis and the risk of a composite of major postoperative cardiovascular morbidity events within 30 days of surgery. Design, Setting, and Participants: This single-center, retrospective cohort study was conducted among 3997 adult patients (aged ≥18 years) with a previous diagnosis of COVID-19, as documented by a positive polymerase chain reaction test result, who were undergoing surgery from January 1, 2020, to December 6, 2021. Data were obtained through Structured Query Language access of an existing perioperative data warehouse. Statistical analysis was performed March 29, 2022. Exposure: The time interval between COVID-19 diagnosis and surgery. Main Outcomes and Measures: The primary outcome was the composite occurrence of major cardiovascular comorbidity, defined as deep vein thrombosis, pulmonary embolism, cerebrovascular accident, myocardial injury, acute kidney injury, and death within 30 days after surgery, using multivariable logistic regression. Results: A total of 3997 patients (2223 [55.6%]; median age, 51.3 years [IQR, 35.1-64.4 years]; 667 [16.7%] African American or Black; 2990 [74.8%] White; and 340 [8.5%] other race) were included in the study. The median time from COVID-19 diagnosis to surgery was 98 days (IQR, 30-225 days). Major postoperative adverse cardiovascular events were identified in 485 patients (12.1%). Increased time from COVID-19 diagnosis to surgery was associated with a decreased rate of the composite outcome (adjusted odds ratio, 0.99 [per 10 days]; 95% CI, 0.98-1.00; P = .006). This trend persisted for the 1552 patients who had received at least 1 dose of COVID-19 vaccine (adjusted odds ratio, 0.98 [per 10 days]; 95% CI, 0.97-1.00; P = .04). Conclusions and Relevance: This study suggests that increased time from COVID-19 diagnosis to surgery was associated with a decreased odds of experiencing major postoperative cardiovascular morbidity. This information should be used to better inform risk-benefit discussions concerning optimal surgical timing and perioperative outcomes for patients with a history of COVID-19 infection.

Protocol for a randomised controlled trial: reducing reintubation among high-risk cardiac surgery patients with high-flow nasal cannula (I-CAN).

INTRODUCTION: Heated, humidified, high-flow nasal cannula oxygen therapy has been used as a therapy for hypoxic respiratory failure in numerous clinical settings. To date, limited data exist to guide appropriate use following cardiac surgery, particularly among patients at risk for experiencing reintubation. We hypothesised that postextubation treatment with high-flow nasal cannula would decrease the all-cause reintubation rate within the 48 hours following initial extubation, compared with usual care. METHODS AND ANALYSIS: Adult patients undergoing cardiac surgery (open surgery on the heart or thoracic aorta) will be automatically enrolled, randomised and allocated to one of two treatment arms in a pragmatic randomised controlled trial at the time of initial extubation. The two treatment arms are administration of heated, humidified, high-flow nasal cannula oxygen postextubation and usual care (treatment at the discretion of the treating provider). The primary outcome will be all-cause reintubation within 48 hours of initial extubation. Secondary outcomes include all-cause 30-day mortality, hospital length of stay, intensive care unit length of stay and ventilator-free days. Interaction analyses will be conducted to assess the differential impact of the intervention within strata of predicted risk of reintubation, calculated according to our previously published and validated prognostic model. ETHICS AND DISSEMINATION: Vanderbilt University Medical Center IRB approval, 15 March 2021 with waiver of written informed consent. Plan for publication of study protocol prior to study completion, as well as publication of results. TRIAL REGISTRATION NUMBER: clinicaltrials.gov, NCT04782817 submitted 25 February 2021. DATE OF PROTOCOL: 29 August 2022. Version 2.0.

Impact of Intraoperative Data on Risk Prediction for Mortality After Intra-Abdominal Surgery.

BACKGROUND: Risk prediction models for postoperative mortality after intra-abdominal surgery have typically been developed using preoperative variables. It is unclear if intraoperative data add significant value to these risk prediction models. METHODS: With IRB approval, an institutional retrospective cohort of intra-abdominal surgery patients in the 2005 to 2015 American College of Surgeons National Surgical Quality Improvement Program was identified. Intraoperative data were obtained from the electronic health record. The primary outcome was 30-day mortality. We evaluated the performance of machine learning algorithms to predict 30-day mortality using: 1) baseline variables and 2) baseline + intraoperative variables. Algorithms evaluated were: 1) logistic regression with elastic net selection, 2) random forest (RF), 3) gradient boosting machine (GBM), 4) support vector machine (SVM), and 5) convolutional neural networks (CNNs). Model performance was evaluated using the area under the receiver operator characteristic curve (AUROC). The sample was randomly divided into a training/testing split with 80%/20% probabilities. Repeated 10-fold cross-validation identified the optimal model hyperparameters in the training dataset for each model, which were then applied to the entire training dataset to train the model. Trained models were applied to the test cohort to evaluate model performance. Statistical significance was evaluated using P < .05. RESULTS: The training and testing cohorts contained 4322 and 1079 patients, respectively, with 62 (1.4%) and 15 (1.4%) experiencing 30-day mortality, respectively. When using only baseline variables to predict mortality, all algorithms except SVM (area under the receiver operator characteristic curve [AUROC], 0.83 [95% confidence interval {CI}, 0.69-0.97]) had AUROC >0.9: GBM (AUROC, 0.96 [0.94-1.0]), RF (AUROC, 0.96 [0.92-1.0]), CNN (AUROC, 0.96 [0.92-0.99]), and logistic regression (AUROC, 0.95 [0.91-0.99]). AUROC significantly increased with intraoperative variables with CNN (AUROC, 0.97 [0.96-0.99]; P = .047 versus baseline), but there was no improvement with GBM (AUROC, 0.97 [0.95-0.99]; P = .3 versus baseline), RF (AUROC, 0.96 [0.93-1.0]; P = .5 versus baseline), and logistic regression (AUROC, 0.94 [0.90-0.99]; P = .6 versus baseline). CONCLUSIONS: Postoperative mortality is predicted with excellent discrimination in intra-abdominal surgery patients using only preoperative variables in various machine learning algorithms. The addition of intraoperative data to preoperative data also resulted in models with excellent discrimination, but model performance did not improve.

A Predictive Model of Reintubation After Cardiac Surgery Using the Electronic Health Record.

BACKGROUND: Reintubation and prolonged intubation after cardiac surgery are associated with significant complications. Despite these competing risks, providers frequently extubate patients with limited insight into the risk of reintubation at the time of extubation. Achieving timely, successful extubation remains a significant clinical challenge. METHODS: Based on an analysis of 2835 patients undergoing cardiac surgery at our institution between November 2017 and July 2020, we developed a model for an individual's risk of reintubation at the time of extubation. Predictors were screened for inclusion in the model based on clinical plausibility and availability at the time of extubation. Rigorous data reduction methods were used to create a model that could be easily integrated into clinical workflow at the time of extubation. RESULTS: In total, 90 patients (3.2%) were reintubated within 48 hours of initial extubation. Number of inotropes (1 [adjusted odds ratio (OR), 15.4; 95% confidence interval (CI) 6.5-47.6; P < .001], ≥2 [OR, 62.7; 95% CI 14.3-279.5; P < .001]); dexmedetomidine dose (OR, 3.0 [per µg/kg/h]; 95% CI 1.9-4.7; P < .001), time to extubation (OR, 1.04 [per 6-hour increase]; 95% CI 1.02-1.05; P < .001), and respiratory rate (OR, 1.04 [per breath/min]; 95% CI 1.01-1.07; P < .001) were the best predictors for the model, which displayed excellent discriminative capacity (area under the receiver operating characteristic, 0.86; 95% CI 0.84-0.89). CONCLUSIONS: An improved understanding of reintubation risk may lead to improved decision-making at extubation and targeted interventions to decrease reintubation in high-risk patients. Future studies are needed to optimize timing of extubation.

Understanding the Accuracy of Clinician Provided Estimated Discharge Dates.

Discharge planning is a vital tool in managing hospital capacity, which is essential for maintaining hospital throughput for surgical postoperative admissions. Early discharge planning has been effective in reducing length of stay and hospital readmissions. Between 2014 and 2017, Vanderbilt University Medical Center (VUMC) implemented a tool in the electronic health record (EHR) requiring providers to input the patient's estimated discharge date on each hospital day. We hypothesized discharge estimates would be more accurate, on average, for surgical patients compared to non-surgical patients because treatment plans are known in advance of surgical admissions. We also analyzed the data to identify factors associated with more accurate discharge estimates. In this retrospective observational study, via an analysis of covariance (ANCOVA) approach, we identified factors associated with more accurate discharge estimates for admitted adult patients at VUMC. The primary outcome was the difference between estimated and actual discharge date, and the primary exposure of interest was whether the patient underwent surgery while admitted to the hospital. A total of 304,802 date of discharge estimate entries from 68,587 inpatient encounters met inclusion criteria. After controlling for measured confounding, we found the discharge estimates were more precise as the difference between estimated and actual discharge date narrowed; for each additional day closer to discharge, prediction accuracy improved by .67 days (95% confidence interval [CI], 0.66 to 0.67; p < 0.001), on average. No difference was observed on the primary outcome in patients undergoing surgery compared with non-surgical treatment (0.02 days; 95% CI, 0.00 to 0.03; p = 0.111). Faculty members were found to perform best among all clinicians in predicting estimated discharge date with a 0.24-day better accuracy (95% CI, 0.20 to 0.27; p < 0.001), on average, than other staff. Weekend and holiday, specific clinical teams, staff types, and discharge dispositions were associated with the variability in estimated versus actual discharge date (p < 0.001). Given the widespread variation in current efforts to improve discharge planning and the recommended approach of assigning a discharge date early in the hospital stay, understanding provider estimated discharge dates is an important tool in hospital capacity management. While we did not determine a difference in discharge estimates among surgical and non-surgical patients, we found estimates were more accurate as discharge came nearer and identified notable trends in provider inputs and patient factors. Assessing factors that impact variability in discharge accuracy can allow hospitals to design targeted interventions to improve discharge planning and reduce unnecessary hospital days.

Etiology and Timing of Postoperative Rapid Response Team Activations.

In this retrospective cohort study we sought to evaluate the association between the etiology and timing of rapid response team (RRT) activations in postoperative patients at a tertiary care hospital in the southeastern United States. From 2010 to 2016, there were 2,390 adult surgical inpatients with RRT activations within seven days of surgery. Using multivariable linear regression, we modeled the correlation between etiology of RRT and timing of the RRT call, as measured from the conclusion of the surgical procedure. We found that respiratory triggers were associated with an increase in time after surgical procedure to RRT of 10.6 h compared to activations due to general concern (95% CI 3.9 - 17.3) (p = 0.002). These findings may have an impact on monitoring of postoperative patients, as well as focusing interventions to better respond to clinically deteriorating patients.