Intensive care unit versus high dependency care unit admission after emergency surgery: a nationwide in-patient registry study

Associations of the staffing structure of intensive care units and high care units on in-hospital mortality among patients with sepsis: a cross-sectional study of Japanese nationwide claims data

OBJECTIVE

The objective was to analyse the associations of intensive care unit (ICU) and high care unit (HCU) organisational structure on in-hospital mortality among patients with sepsis in Japan's acute care hospitals.

DESIGN

Multicentre cross-sectional study.

SETTINGS

Patients with sepsis aged ≥18 years who received critical care in acute care hospitals throughout Japan between April 2018 and March 2019 were identified using the National Database of Health Insurance Claims and Specific Health Checkups of Japan (NDB).

INTERVENTIONS

None.

PARTICIPANTS

10 968 patients with sepsis were identified. ICUs were categorised into three groups: type 1 ICUs (fulfilling stringent staffing criteria such as experienced intensivists and high nurse-to-patient ratios), type 2 ICUs (less stringent criteria) and HCUs (least stringent criteria).

PRIMARY OUTCOME MEASURE

The study's primary outcome measure was in-hospital mortality. Cox proportional hazards regression analysis was performed to examine the impact of ICU/HCU groups on in-hospital mortality.

RESULTS

We analysed 2411 patients (178 hospitals) in the type 1 ICU group, 3653 patients (422 hospitals) in the type 2 ICU group and 4904 patients (521 hospitals) in the HCU group. When compared with the type 1 ICU group, the adjusted HRs for in-hospital mortality were 1.12 (95% CI 1.04 to 1.21) for the type 2 ICU group and 1.17 (95% CI 1.08 to 1.26) for the HCU group.

CONCLUSION

ICUs that fulfil more stringent staffing criteria were associated with lower in-hospital mortality among patients with sepsis than HCUs. Differences in organisational structure may have an association with outcomes in patients with sepsis, and this was observed by the NDB.

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.

The Role of Intermediate Care in Supporting Critically Ill Patients and Critical Care Infrastructure

Intermediate care (IC) is used for patients who do not require the human and technological support of the intensive care unit (ICU) yet require more care and monitoring than can be provided on general wards. Though prevalent in many countries, there is marked variability in models of organization and staffing, as well as monitoring and interventions provided. In this article, the authors will discuss the historical background of IC, review the impact of IC on ICU and IC patient outcomes, and highlight where future studies can shed light on how to optimize IC organization and outcomes.

EPidemiology, clinical characteristics and Outcomes of 4546 adult admissions to high-dependency and intensive care units in Kenya (EPOK): a multicentre registry-based observational study

Objective

to describe clinical, management and outcome features of critically ill patients admitted to intensive care units (ICUs) and high dependency units (HDUs) in Kenya.

Design

prospective registry-based observational study.

Setting

three HDUs and eight ICUs in Kenya.

Patients

consecutive adult patients admitted between January 2021 and June 2022.

Interventions

none.

Measurements and main results

data was entered in a cloud based platform using a common data model. Study endpoints included case mix variables, management features and patient centred outcomes. Patients with Coronavirus disease 2019 (COVID-19) were reported separately. Of the 3892/4546 patients without COVID-19, 2445 patients (62.8%) were from HDUs and 1447 (37.2%) from ICUs. Patients had a median age of 53 years (interquartile range [IQR] 38-68), with HDU patients being older but with a lower severity (APACHE II 6 [3-9] in HDUs vs 12 [7-17] in ICUs; p<0.001). One out of four patients were postoperative with 604 (63.4%) receiving emergency surgery. Readmission rate was 4.8%. Hypertension and diabetes were prevalent comorbidities, with a 4.0% HIV/AIDS rate. Invasive mechanical ventilation (IMV) was applied in 3.4% in HDUs vs. 47.6% in ICUs (P<0.001), with a duration of 7 days (IQR 3-21). There was a similar use of renal replacement therapy (4.0% vs. 4.7%; P<0.001). Vasopressor use was infrequent while half of patients received antibiotics. Average length of stay was 2 days (IQR 1-5). Crude HDU mortality rate was 6.5% in HDUs versus 30.5% in the ICUs (P<0.001). Of the 654 COVID-19 admissions, most were admitted in ICUs (72.3%) with a 33.2% mortality.

Conclusions

We provide the first multicenter observational cohort study from an African ICU national registry. Distinct management features and outcomes characterise HDU from ICU patients.

Study registration

Clinicaltrials.gov (reference number NCT05456217, date of registration 07 Nov 2022).

Correlation between mortality and blood transfusion in patients with major surgery initially admitted to intensive care unit: a retrospective analysis

PURPOSE

Transfusing red blood cells promptly corrects anemia and improves tissue oxygenation in around 40% of patients hospitalized in the intensive care unit (ICU) after major surgical operations. This study's goal is to investigate how blood transfusions affect the mortality rates of patients after major surgery who are hospitalized in the ICU.

METHODS

Retrospective research was done on recently hospitalized patients who had major procedures in the ICU between October 2020 and February 2022 at the Huanggang Central Hospital of Yangtze University, China. The patients' prognoses at three months were used to classify them as either survivors or deceased. Patient demographic information, laboratory results, and blood transfusion histories were acquired, and the outcomes of the two groups were compared based on the differences. Univariate and multivariate logistic regression analyses were used to examine the prognosis of surgical disease patients first admitted to the ICU. The receiver operating characteristic (ROC) curve was used to evaluate the predictive power of each risk factor. The relationship between transfusion frequency, transfusion modality, and patient outcome was examined using Spearman's correlation analysis.

RESULTS

Data from 384 patients was included in the research; of them, 214 (or 55.7%) died within three months of their first stay in the ICU. The death group had higher scores on the Acute Physiology and Chronic Health Evaluation II (APACHE II) and the Sequential Organ Failure Assessment (SOFA) than the survival group did (all P < 0.05); the death group also had lower scores on the Glasgow Coma Scale, systolic blood pressure, hemoglobin, platelet distribution width, and blood transfusion ratio. Multivariate logistic regression analysis revealed an odds ratio (OR) of 1.654 (1.281-1.989), a 95% confidence interval (CI) of 1.440 (1.207-1.701), and a P value of 0.05 for death in patients undergoing major surgery who were hospitalized to the intensive care unit (ICU). Areas under the ROC curve (AUC) of 0.836, 0.799, and 0.871, respectively, and 95% CIs of 0.796-0.875, 0.755-0.842, and 0.837-0.904, respectively, all P0.05, had significant predictive value for patients initially admitted to the ICU and for APACHE II score > = 12 points, SOFA score > = 6, and blood transfusion. When all three indicators were used jointly to predict a patient's prognosis after major surgery, the accuracy increased to 86.4% (sensitivity) and 100% (specificity). There was a negative correlation between the number of blood transfusions a patient had and their outcome (r = 0.605, P < 0.001) and death (r = 0.698, P < 0.001).

CONCLUSION

A higher initial ICU APACHE II score, SOFA score, and a number of blood transfusions were associated with improved survival for patients undergoing major surgical operations. Patients' death rates have increased with the increase in the frequency and variety of blood transfusions.