Incidence of early major adverse events after surgery in moderate-risk patients: early postoperative adverse events

Outcomes of Postoperative Overnight High-Acuity Care in Medium-Risk Patients Undergoing Elective and Unplanned Noncardiac Surgery

Importance

Postoperative complications are increasing, risking patients' health and health care sustainability. High-acuity postoperative units may benefit outcomes, but existing data are very limited.

Objective

To evaluate whether a new high-acuity postoperative unit, advanced recovery room care (ARRC), reduces complications and health care utilization compared with usual ward care (UC).

Design, Setting, and Participants

In this observational cohort study, adults who were undergoing noncardiac surgery at a single-center tertiary adult hospital, anticipated to stay in hospital for 2 or more nights, were scheduled for postoperative ward care, and at medium risk (defined as predicted 30-day mortality of 0.7% to 5% by the National Safety Quality Improvement Program risk calculator) were included. Allocation to ARRC was based on bed availability. From 2405 patients assessed for eligibility with National Safety Quality Improvement Program risk scoring, 452 went to ARRC and 419 to UC, with 8 lost to 30-day follow-up. Propensity scoring identified 696 patients with matched pairs. Patients were treated between March and November 2021, and data were analyzed from January to September 2022.

Interventions

ARRC is an extended postanesthesia care unit (PACU), staffed by anesthesiologists and nurses (1 nurse to 2 patients) collaboratively with surgeons, with capacity for invasive monitoring and vasoactive infusions. ARRC patients were treated until the morning after surgery, then transferred to surgical wards. UC patients were transferred to surgical wards after usual PACU care.

Main Outcome and Measures

The primary end point was days at home at 30 days. Secondary end points were health facility utilization, medical emergency response (MER)-level complications, and mortality. Analyses compared groups before and after propensity scoring matching.

Results

Of 854 included patients, 457 (53.5%) were male, and the mean (SD) age was 70.0 (14.4) years. Days at home at 30 days was greater with ARRC compared with UC (mean [SD] time, 17 [11] vs 15 [11] days; P = .04). During the first 24 hours, more patients were identified with MER-level complications in ARRC (43 [12.4%] vs 13 [3.7%]; P < .001), but after return to the ward, these were less frequent from days 2 to 9 (9 [2.6%] vs 22 [6.3%]; P = .03). Length of hospital stay, hospital readmissions, emergency department visits, and mortality were similar.

Conclusions and Relevance

For medium-risk patients, brief high-acuity care with ARRC allowed enhanced detection and management of early MER-level complications, which was followed by a decreased incidence of subsequent MER-level complications after discharge to the ward and by increased days at home at 30 days.

External validation of a surgical mortality risk prediction model for inpatient noncardiac surgery in an Australian private health insurance dataset

BACKGROUND

We previously conducted a systematic review to identify surgical mortality risk prediction tools suitable for adapting in the Australian context and identified the Surgical Outcome Risk Tool (SORT) as an ideal model. The primary aim was to investigate the external validity of SORT for predicting in-hospital mortality in a large Australian private health insurance dataset.

METHODS

A cohort study using a prospectively collected Australian private health insurance dataset containing over 2 million deidentified records. External validation was conducted by applying the predictive equation for SORT to the complete case analysis dataset. Model re-estimation (recalibration) was performed by logistic regression.

RESULTS

The complete case analysis dataset contained 161 277 records. In-hospital mortality was 0.2% (308/161277). The mean estimated risk given by SORT was 0.2% and the median (IQR) was 0.01% (0.003%-0.08%). Discrimination was high (c-statistic 0.96) and calibration was accurate over the range 0%-10%, beyond which mortality was over-predicted but confidence intervals included or closely approached the perfect prediction line. Re-estimation of the equation did not improve over-prediction. Model diagnostics suggested the presence of outliers or highly influential values.

CONCLUSION

The low perioperative mortality rate suggests the dataset was not representative of the overall Australian surgical population, primarily due to selection bias and classification bias. Our results suggest SORT may significantly under-predict 30-day mortality in this dataset. Given potential differences in perioperative mortality, private health insurance status and hospital setting should be considered as covariables when a locally validated national surgical mortality risk prediction model is developed.

Enhanced peri-operative care to improve outcomes for high-risk surgical patients in Brazil: a single-centre before-and-after cohort study

Mortality and morbidity for high-risk surgical patients are often high, especially in low-resource settings. Enhanced peri-operative care has the potential to reduce preventable deaths but must be designed to meet local needs. This before-and-after cohort study aimed to assess the effectiveness of a postoperative 48-hour enhanced care pathway for high-risk surgical patients ('high-risk surgical bundle') who did not meet the criteria for elective admission to intensive care. The pathway comprised of six elements: risk identification and communication; adoption of a high-risk post-anaesthesia care unit discharge checklist; prompt nursing admission to ward; intensification of vital signs monitoring; troponin measurement; and prompt access to medical support if required. The primary outcome was in-hospital mortality. Data describing 1189 patients from two groups, before and after implementation of the pathway, were compared. The usual care group comprised a retrospective cohort of high-risk surgical patients between September 2015 and December 2016. The intervention group prospectively included high-risk surgical patients from February 2019 to March 2020. Unadjusted mortality rate was 10.5% (78/746) for the usual care and 6.3% (28/443) for the intervention group. After adjustment, the intervention effect remained significant (RR 0.46 (95%CI 0.30-0.72). The high-risk surgical bundle group received more rapid response team calls (24% vs. 12.6%; RR 0.63 [95%CI 0.49-0.80]) and surgical re-interventions (18.9 vs. 7.5%; RR 0.41 [95%CI 0.30-0.59]). These data suggest that a clinical pathway based on enhanced surveillance for high-risk surgical patients in a resource-constrained setting could reduce in-hospital mortality.

The impact of the incorporation of a feasible postoperative mortality model at the Post-Anaesthestic Care Unit (PACU) on postoperative clinical deterioration: A pragmatic trial with 5,353 patients

Background

Practical use of risk predictive tools and the assessment of their impact on outcome reduction is still a challenge. This pragmatic study of quality improvement (QI) describes the preoperative adoption of a customised postoperative death probability model (SAMPE model) and the evaluation of the impact of a Postoperative Anaesthetic Care Unit (PACU) pathway on the clinical deterioration of high-risk surgical patients.

Methods

A prospective cohort of 2,533 surgical patients compared with 2,820 historical controls after the adoption of a quality improvement (QI) intervention. We carried out quick postoperative high-risk pathways at PACU when the probability of postoperative death exceeded 5%. As outcome measures, we used the number of rapid response team (RRT) calls within 7 and 30 postoperative days, in-hospital mortality, and non-planned Intensive Care Unit (ICU) admission.

Results

Not only did the QI succeed in the implementation of a customised risk stratification model, but it also diminished the postoperative deterioration evaluated by RRT calls on very high-risk patients within 30 postoperative days (from 23% before to 14% after the intervention, p = 0.05). We achieved no survival benefits or reduction of non-planned ICU. The small group of high-risk patients (13% of the total) accounted for the highest proportion of RRT calls and postoperative death.

Conclusion

Employing a risk predictive tool to guide immediate postoperative care may influence postoperative deterioration. It encouraged the design of pragmatic trials focused on feasible, low-technology, and long-term interventions that can be adapted to diverse health systems, especially those that demand more accurate decision making and ask for full engagement in the control of postoperative morbi-mortality.

The Hidden Pandemic: the Cost of Postoperative Complications

Purpose of Review

Population-based increases in ageing and medical co-morbidities are expected to substantially increase the incidence of expensive postoperative complications. This threatens the sustainability of essential surgical care, with negative impacts on patients' health and wellbeing.

Recent Findings

Identification of key high-risk areas, and implementation of proven cost-effective strategies to manage both outcome and cost across the end-to-end journey of the surgical episode of care, is clearly feasible. However, good programme design and formal cost-effectiveness analysis is critical to identify, and implement, true high value change.

Summary

Both outcome and cost need to be a high priority for both fundholders and clinicians in perioperative care, with the focus for both groups on delivering high-quality care, which in itself, is the key to good cost management.

The effect of advanced recovery room care on postoperative outcomes in moderate-risk surgical patients: a multicentre feasibility study

Postoperative complications are common and may be under-recognised. It has been suggested that enhanced postoperative care in the recovery room may reduce in-hospital complications in moderate- and high-risk surgical patients. We investigated the feasibility of providing advanced recovery room care for 12-18 h postoperatively in the post-anaesthesia care unit. The primary hypothesis was that a clinical trial of advanced recovery room care was feasible. The secondary hypothesis was that this model may have a sustained impact on postoperative in-hospital and post-discharge events. This was a multicentre, prospective, feasibility before-and-after trial of moderate-risk patients (predicted 30-day mortality of 1-4%) undergoing non-cardiac surgery and who were scheduled for postoperative ward care. Patients were managed using defined assessment checklists and goals of care in an advanced recovery room care setting in the immediate postoperative period. This utilised existing post-anaesthesia care unit infrastructure and staffing, but extended care until the morning of the first postoperative day. The advanced recovery room care trial was deemed feasible, as defined by the recruitment and per protocol management of > 120 patients. However, in a specialised cancer centre, recruitment was slow due to low rates of eligibility according to narrow inclusion criteria. At a rural site, advanced recovery room care could not be commenced due to logistical issues in establishing a new model of care. A definitive randomised controlled trial of advanced recovery room care appears feasible and, based on the indicative data on outcomes, we believe this is warranted.