Characteristics and outcomes of unplanned intensive care unit admission after general anesthesia

Machine Learning Predicts Unplanned Care Escalations for Post-Anesthesia Care Unit Patients during the Perioperative Period: A Single-Center Retrospective Study

BACKGROUND

 Despite low mortality for elective procedures in the United States and developed countries, some patients have unexpected care escalations (UCE) following post-anesthesia care unit (PACU) discharge. Studies indicate patient risk factors for UCE, but determining which factors are most important is unclear. Machine learning (ML) can predict clinical events. We hypothesized that ML could predict patient UCE after PACU discharge in surgical patients and identify specific risk factors.

METHODS

We conducted a single center, retrospective analysis of all patients undergoing non-cardiac surgery (elective and emergent). We collected data from pre-operative visits, intra-operative records, PACU admissions, and the rate of UCE. We trained a ML model with this data and tested the model on an independent data set to determine its efficacy. Finally, we evaluated the individual patient and clinical factors most likely to predict UCE risk.

RESULTS

Our study revealed that ML could predict UCE risk which was approximately 5% in both the training and testing groups. We were able to identify patient risk factors such as patient vital signs, emergent procedure, ASA Status, and non-surgical anesthesia time as significant variable. We plotted Shapley values for significant variables for each patient to help determine which of these variables had the greatest effect on UCE risk. Of note, the UCE risk factors identified frequently by ML were in alignment with anesthesiologist clinical practice and the current literature.

CONCLUSIONS

We used ML to analyze data from a single-center, retrospective cohort of non-cardiac surgical patients, some of whom had an UCE. ML assigned risk prediction for patients to have UCE and determined perioperative factors associated with increased risk. We advocate to use ML to augment anesthesiologist clinical decision-making, help decide proper disposition from the PACU, and ensure the safest possible care of our patients.

Pro-Con Debate: Universal Versus Selective Continuous Monitoring of Postoperative Patients

In this Pro-Con commentary article, we discuss use of continuous physiologic monitoring for clinical deterioration, specifically respiratory depression in the postoperative population. The Pro position advocates for 24/7 continuous surveillance monitoring of all patients starting in the postanesthesia care unit until discharge from the hospital. The strongest arguments for universal monitoring relate to inadequate assessment and algorithms for patient risk. We argue that the need for hospitalization in and of itself is a sufficient predictor of an individual's risk for unexpected respiratory deterioration. In addition, general care units carry the added risk that even the most severe respiratory events will not be recognized in a timely fashion, largely due to higher patient to nurse staffing ratios and limited intermittent vital signs assessments (e.g., every 4 hours). Continuous monitoring configured properly using a "surveillance model" can adequately detect patients' respiratory deterioration while minimizing alarm fatigue and the costs of the surveillance systems. The Con position advocates for a mixed approach of time-limited continuous pulse oximetry monitoring for all patients receiving opioids, with additional remote pulse oximetry monitoring for patients identified as having a high risk of respiratory depression. Alarm fatigue, clinical resource limitations, and cost are the strongest arguments for selective monitoring, which is a more targeted approach. The proponents of the con position acknowledge that postoperative respiratory monitoring is certainly indicated for all patients, but not all patients need the same level of monitoring. The analysis and discussion of each point of view describes who, when, where, and how continuous monitoring should be implemented. Consideration of various system-level factors are addressed, including clinical resource availability, alarm design, system costs, patient and staff acceptance, risk-assessment algorithms, and respiratory event detection. Literature is reviewed, findings are described, and recommendations for design of monitoring systems and implementation of monitoring are described for the pro and con positions.

Unexpected intensive care unit admission after surgery: impact on clinical outcome

PURPOSE OF REVIEW

This review is focused on providing insights into unplanned admission to the intensive care unit (ICU) after surgery, including its causes, effects on clinical outcome, and potential strategies to mitigate the strain on healthcare systems.

RECENT FINDINGS

Postoperative unplanned ICU admission results from a combination of several factors including patient's clinical status, the type of surgical procedure, the level of supportive care and clinical monitoring outside the ICU, and the unexpected occurrence of major perioperative and postoperative complications. The actual impact of unplanned admission to ICU after surgery on clinical outcome remains uncertain, given the conflicting results from several observational studies and recent randomized clinical trials. Nonetheless, unplanned ICU admission after surgery results a significant strain on hospital resources. Consequently, this issue should be addressed in hospital policy with the aim of implementing preoperative risk assessment and patient evaluation, effective communication, vigilant supervision, and the promotion of cooperative healthcare.

SUMMARY

Unplanned ICU admission after surgery is a multifactorial phenomenon that imposes a significant burden on healthcare systems without a clear impact on clinical outcome. Thus, the early identification of patient necessitating ICU interventions is imperative.

Efficacy, safety, and side effects of oliceridine in acute postoperative pain, a protocol for a systematic review and meta-analysis

This will be the first meta-analysis on the efficacy, safety, and side effects of oliceridine on postoperative pain. Our aim with this work is to evaluate the clinical utility of this relatively new substance in a broad postoperative context. Oliceridine is a new so-called bias opioid that is approved for severe pain requiring an opioid. Due to its biased agonism, it is said to have fewer side effects than conventional opioids. This systematic review and meta-analysis will analyze the efficacy, safety, and side effects of oliceridine compared to placebo or morphine in acute postoperative pain for up to 72 hours. In January 2024, an extensive search in various databases will be performed without restrictions for randomized controlled trials with at least single blinding. After data extraction, data will be pooled and meta-analytic calculations performed. A random-effects model will be used. Dichotomous data will be presented as risk ratio and continuous data as standardized mean difference. Dose-dependent side effects will be evaluated with meta-regression. Heterogeneity will be assessed via the Q statistic and prediction interval in case of a sufficient number of included studies. Publication bias will be examined using funnel plot and Duval and Tweedie's trim and fill method.

Outcomes in very elderly ICU patients surgically treated for proximal femur fractures

Proximal femur fractures (PFF) are a common injury in elderly patients that significantly impact mobility and daily living activities. Mortality rates in this population are also high, making effective treatment essential. Recent advances in intensive and geriatric care have enabled complex surgical interventions that were previously not feasible. However, there is a lack of studies focusing on outcome parameters in very elderly patients (≥ 90 years) who receive intensive care treatment following PFFs. In this retrospective study, we analyzed multi-layered data of 148 patients who were 90 years or older and received intensive care after trauma and orthopedic surgical treatment for PFFs or periprosthetic fractures between 2009 and 2019. All patients received a 365-day follow-up. To identify potential predictors of mortality, all deceased and surviving patients were subjected to multiple logistic regression analyses. We found that 22% of patients deceased during in-hospital care, and one-year survival was 44%. Independent predictors of one-year all-cause mortality included higher CCI and SOFA scores at ICU admission. Overall, 53% of patients who resided in private dwellings prior to admission were able to return home. Our study highlights the utility of using CCI and SOFA scores at ICU admission as prognostic indicators in critically ill very elderly patients who undergo surgical treatment for PFFs. These scores can provide valuable insight into the severity of illness and potential outcomes, which can inform resource allocation, prioritize endangered patients, and aid in end-of-life discussions and planning with patients and their families. Our findings can help improve the management of PFFs in very elderly patients and contribute to optimized patient care.

Delayed discharge from post-anesthesia care unit: A 20-case retrospective series

OBJECTIVE

To summarize the characteristics of patients with delayed discharge from the post-anesthesia care unit and to analyze the factors and outcomes of delayed discharge.

METHODS

Twenty cases of delayed discharge from the PACU (PACU stay >2 hours after surgery) of the main operating room in Liaocheng People's Hospital, a class A tertiary comprehensive hospital, between January 1, 2021, and December 31, 2022, among 28,084 patients who were transferred to the PACU from the operating rooms after surgery, were retrospectively analyzed. The collected data included patient characteristics, American society of anesthesiologists grade, information related to surgery and anesthesia, and outcomes. The factors for delay were assigned to 1 of 6 groups: delayed recovery from anesthesia, surgical complications, cardiovascular instability, hypoxia, inadequate analgesia, and waiting for the operating room.

RESULTS

The incidence of delayed discharge from PACU was 0.7‰. Among 20 patients, more than half of the patients were over 65 years of age, American society of anesthesiologists grade II~III, body mass index <30 kg/m2, and urological surgery (7, 35%), liver surgery (4, 20%), thoracic surgery (4, 20%) accounted for a relatively high proportion. Nineteen (95%) patients received general anesthesia with or without peripheral nerve block. The main factors included delayed recovery from anesthesia (6, 30%), surgical complications (5, 25%), cardiovascular complications (4, 20%), hypoxia (3,15%). After discharge from the PACU, 1 (5%) died in the intensive care unit, and the other 19 (95%) patients were safely discharged from the hospital.

CONCLUSION

The incidence of delayed discharge from the PACU was low, and it was more likely to occur in the elderly, during major operations, and under general anesthesia. Delayed recovery from anesthesia was the most common factor. Most patients were safely discharged from the hospital.

Patient Characteristics and Clinical and Economic Outcomes Associated with Unplanned Medical and Surgical Intensive Care Unit Admissions: A Retrospective Analysis

Purpose

To characterize medical and surgical patient characteristics, as well as clinical and economic outcomes, associated with unplanned intensive care unit (ICU) admissions.

Patients and Methods

This was a retrospective matched cohort analysis that utilized the PINC AITM Healthcare Database, which collects deidentified data from 25% of United States (US) hospital admissions. Discharge records were assessed for medical and surgical admissions in 2021. An unplanned ICU admission was defined as direct transfer from a medical, surgical, or telemetry unit to the ICU. Patients with and without an unplanned ICU admission were 1:1 propensity score matched. Differences between patients with and without unplanned ICU admissions were assessed using two-sample t-tests for continuous measures and Chi-square tests for categorical measures.

Results

A total of 3,807,124 qualifying admissions were identified. Medical admissions with unplanned ICU transfers were more likely to be urgent/emergent (odds ratio [OR] 2.9, 95% confidence interval [CI 2.7-3.0], p<0.0001), with patient characteristics including male sex (1.4, [1.4-1.4], p<0.0001), obesity (1.7, [1.6-1.7], p<0.0001), and increased Charlson Comorbidity Index (CCI=1: 1.8, [1.8-1.9], p<0.0001; CCI≥5: 3.2, [3.1-3.3], p<0.0001). Surgical admissions with unplanned ICU transfers were more likely to be urgent/emergent (3.1, [2.9-3.2], p<0.0001) and with patients of higher CCI (2.5, [2.3-2.6], p<0.0001 to a CCI of≥5 (7.9, [7.4-8.4], p<0.0001). Between matched medical patients, mean differences in length of stay, cost, and mortality were 4.1 days (p<0.0001), $13,424 (p<0.0001), and 21% (p<0.0001), respectively. Between matched surgical patients, mean differences in these outcomes were 6.4 days (p<0.0001), $21,448 (p<0.0001), and 14% (p<0.0001), respectively.

Conclusion

Emergency care in patients with a higher co-morbid burden is more likely to lead to unplanned ICU admission, putting patients at a significantly increased chance of mortality, longer length of stay, and increased costs. Improving care and monitoring of patients outside the ICU may help detect early changes in pathophysiology and enable early intervention.