Using predicted 30 day mortality to plan postoperative colorectal surgery care: a cohort study

Critical care unit bed availability and postoperative outcomes: a multinational cohort study

BACKGROUND

Critical care beds are a limited resource, yet research indicates that recommendations for postoperative critical care admission based on patient-level risk stratification are not followed. It is unclear how prioritisation decisions are made in real-world settings and the effect of this prioritisation on outcomes.

METHODS

This was a prespecified analysis of an observational cohort study of adult patients undergoing inpatient surgery, conducted in 274 hospitals across the UK and Australasia during 2017. The primary outcome was postoperative morbidity at day 7. Logistic regression models were used to evaluate the relationship between critical care admission and patient and health system factors. The causal effect of critical care admission on outcome was estimated using variation in critical care occupancy as a natural experiment in an instrumental variable analysis.

RESULTS

A total of 19,491 patients from 248 hospitals were eligible for analysis, of whom 2107 were directly admitted to critical care postoperatively. Postoperative morbidity occurred in 2829/19,491 (15%) patients. Increasing surgical risk was associated with critical care admission, as was increased availability of critical care beds (odds ratio (95%CI) 1.04 (1.01-1.06), p = 0.002) per available bed; however, the probability of admission varied significantly between hospitals (median odds ratio 3.05). There was no evidence of a difference in postoperative morbidity with critical care admission (odds ratio (95%CI) 0.91 (0.57-1.45), p = 0.710).

DISCUSSION

Postoperative critical care admission is variable and related to bed availability. Statistical methods that adjust for unobserved confounding lowered the estimates of harm previously reported to have been associated with postoperative critical care admission. Our findings provide a rationale for a clinical trial which would evaluate any potential benefits for postoperative critical care admission for patients in whom there is no absolute indication for admission.

The Cost-Effectiveness of Early High-Acuity Postoperative Care for Medium-Risk Surgical Patients

BACKGROUND

Initiatives in perioperative care warrant robust cost-effectiveness analysis in a cost-constrained era when high-value care is a priority. A model of anesthesia-led early high-acuity postoperative care, advanced recovery room care (ARRC), has shown benefit in terms of hospital and patient outcomes, but its cost-effectiveness has not yet been formally determined.

METHODS

Data from a previously published single-center prospective cohort study of ARRC in medium-risk patients were used to generate a Markov model, which described patient transition between care locations, each with different characteristics and costs. The incremental cost-effectiveness ratio (ICER), using days at home (DAH) and hospital costs, was calculated for ARRC compared to usual ward care using deterministic and probabilistic sensitivity analysis.

RESULTS

The Markov model accurately described patient disposition after surgery. For each patient, ARRC provided 4.3 more DAH within the first 90 days after surgery and decreased overall hospital costs by $1081 per patient. Probabilistic sensitivity analysis revealed that ARRC had a 99.3% probability of increased DAH and a 77.4% probability that ARRC was dominant from the perspective of the hospital, with improved outcomes and decreased costs.

CONCLUSIONS

Early high-acuity care for approximately 24 hours after surgery in medium-risk patients provides highly cost-effective improvements in outcomes when compared to usual ward care.

Development and validation of 'Patient Optimizer' (POP) algorithms for predicting surgical risk with machine learning

BACKGROUND

Pre-operative risk assessment can help clinicians prepare patients for surgery, reducing the risk of perioperative complications, length of hospital stay, readmission and mortality. Further, it can facilitate collaborative decision-making and operational planning.

OBJECTIVE

To develop effective pre-operative risk assessment algorithms (referred to as Patient Optimizer or POP) using Machine Learning (ML) that predict the development of post-operative complications and provide pilot data to inform the design of a larger prospective study.

METHODS

After institutional ethics approval, we developed a base model that encapsulates the standard manual approach of combining patient-risk and procedure-risk. In an automated process, additional variables were included and tested with 10-fold cross-validation, and the best performing features were selected. The models were evaluated and confidence intervals calculated using bootstrapping. Clinical expertise was used to restrict the cardinality of categorical variables (e.g. pathology results) by including the most clinically relevant values. The models were created with logistic regression (LR) and extreme gradient-boosted trees using XGBoost (Chen and Guestrin, 2016). We evaluated performance using the area under the receiver operating characteristic curve (AUROC) and the area under the precision-recall curve (AUPRC). Data was obtained from a metropolitan university teaching hospital from January 2015 to July 2020. Data collection was restricted to adult patients undergoing elective surgery.

RESULTS

A total of 11,475 adult admissions were included. The performance of XGBoost and LR was very similar across endpoints and metrics. For predicting the risk of any post-operative complication, kidney failure and length-of-stay (LOS), POP with XGBoost achieved an AUROC (95%CI) of 0.755 (0.744, 0.767), 0.869 (0.846, 0.891) and 0.841 (0.833, 0.847) respectively and AUPRC of 0.651 (0.632, 0.669), 0.336 (0.282, 0.390) and 0.741 (0.729, 0.753) respectively. For 30-day readmission and in-patient mortality, POP with XGBoost achieved an AUROC (95%CI) of 0.610 (0.587, 0.635) and 0.866 (0.777, 0.943) respectively and AUPRC of 0.116 (0.104, 0.132) and 0.031 (0.015, 0.072) respectively.

CONCLUSION

The POP algorithms effectively predicted any post-operative complication, kidney failure and LOS in the sample population. A larger study is justified to improve the algorithm to better predict complications and length of hospital stay. A larger dataset may also improve the prediction of additional specific complications, readmission and mortality.

Surgical High Dependency Admissions after Elective Laparoscopic Colorectal Resections: Is It Truly Necessary?

BACKGROUND

Surgical high dependency (SHD) allows for intermediate care provision between general ward (GW) and intensive care unit (ICU) for surgical patients but no universally accepted admission criteria exists. Unnecessary SHD admissions should be minimized to limit resource wastage and maintain spare critical care capacity. This study evaluates the utility of SHD admissions following elective laparoscopic colectomy by comparing post-operative outcomes and interventions performed between SHD and GW patients.

METHODOLOGY

A retrospective review of all colorectal cancer patients who underwent elective laparoscopic colectomy in our institution between January 2019 and December 2021 was conducted. Patients converted to open surgery or admitted to IC post-operatively were excluded. Peri-operative parameters and outcomes between patients admitted to GW and SHD post-operatively were evaluated.

RESULTS

The cohort comprised 393 patients. There were 153 patients (38.93%) who required SHD admission. SHD patients had higher American Society of Anesthesiology (ASA) scores, body mass index, age and intra-operative blood loss. Majority of post-operative morbidity were minor (Clavien-Dindo II or lower) in both groups and the interventions required were safely instituted in both SHD and GW. None of the patients in the cohort required inotropic or ventilatory support in the SHD.

CONCLUSIONS

GW patients were "healthier" but post-operative morbidity and interventions required were similar to the SHD group. Nonetheless, treatment delays, absence of continuous monitoring, and decreased nurse-to-patient ratio may be significant for patients with limited physiological reserves. Further studies should evaluate safety and cost-effectiveness of managing high risk surgical patients in GW using continuous remote vital signs monitoring.

Peak oxygen uptake in combination with ventilatory efficiency improve risk stratification in major abdominal surgery

This pilot study aimed to evaluate if peak VO2 and ventilatory efficiency in combination would improve preoperative risk stratification beyond only relying on peak VO2 . This was a single-center retrospective cohort study including all patients who underwent cardiopulmonary exercise testing (CPET) as part of preoperative risk evaluation before major upper abdominal surgery during years 2008-2021. The primary outcome was any major cardiopulmonary complication during hospitalization. Forty-nine patients had a preoperative CPET before decision to pursue to surgery (cancer in esophagus [n = 18], stomach [6], pancreas [16], or liver [9]). Twenty-five were selected for operation. Patients who suffered any major cardiopulmonary complication had lower ventilatory efficiency (i.e., higher VE/VCO2 slope, 37.3 vs. 29.7, p = 0.031) compared to those without complications. In patients with a low aerobic capacity (i.e., peak VO2  < 20 mL/kg/min) and a VE/VCO2 slope ≥ 39, 80% developed a major cardiopulmonary complication. In this pilot study of patients with preoperative CPET before major upper abdominal surgery, patients who experienced a major cardiopulmonary complication had significantly lower ventilatory efficiency compared to those who did not. A low aerobic capacity in combination with low ventilatory efficiency was associated with a very high risk (80%) of having a major cardiopulmonary complication.

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.

[The Status of Spiroergometry in Preoperative Risk Assessment]

Physical performance is considered as a prognostic factor for perioperative mortality and morbidity. Thus, in recent years, spiroergometry has gained increasing significance in preoperative assessment. Beside the measurement of functional capacity, spiroergometric results may yield indications for cardiocirculatory or pulmonary disorders. A significant increased risk profile is reflected in the values VO₂max < 15 ml/kg/min, VO₂ at the first ventilatory threshold (VT1) < 11 ml/kg/min, and VE/VCO₂ at VT1 > 34. Prior to the examination, contraindications should be taken into account and standardized conduction must be adhered to. Many studies substantiate the positive effect of prehabilitation on morbidity, quality of life, and length of hospitalization, in particular in abdominal, thoracic and hepatobiliary surgery. Using the data acquired in the performance diagnostic, an optimized individual training plan can be drawn up. Besides, the risk evaluation contributes to planning surgical and anaesthetic procedures. Regular training and interdisciplinary teamwork are of utmost importance for the correct interpretation and application of the partly rather complex results.

Characteristics and outcomes of surgical patients admitted to an overnight intensive recovery unit: A retrospective observational study

Postoperative 'enhanced care' models that sit between critical care and ward-based care may allow for more cost-effective and efficient utilisation of resources for high-risk surgical patients. In this retrospective observational study, we describe an overnight intensive recovery model in a tertiary hospital, termed 'recovery high dependency unit', and the characteristics, treatment, disposition at discharge and in-hospital outcomes of patients admitted to this unit. We included all adult patients (≥18 years) admitted to the recovery high dependency unit for at least one hour between July 2017 and June 2020. Over this three-year period, 1257 patients were included in the study. The median length of stay in the recovery high dependency unit was 12.6 (interquartile range 9.1-15.9) hours and the median length of hospital stay was 8.3 (interquartile range 5.0-17.3) days. Hospital discharge data showed that 1027 (81.7%) patients were discharged home and that 37 (2.9%) patients died. Non-invasive ventilation was delivered to 59 (4.7%) patients and 290 (23.1%) required vasopressor support. A total of 164 patients (13.0%) were admitted to the intensive care unit following their recovery high dependency unit admission. Of the 1093 patients who were discharged to the ward, 70 patients (6.4%) had a medical emergency team call within 24 hours of discharge from the recovery high dependency unit. In this study of a recovery high dependency unit patient cohort, there was a relatively low need for intensive care unit admission postoperatively and a very low incidence of medical emergency team calls post-discharge to the ward. Other institutions may consider the introduction and evaluation of this model in the care of their higher risk surgical patients.

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.

The impact of universal cardiopulmonary exercise testing on perioperative pathways and short-term patient outcomes following elective pancreatic surgery: A retrospective cohort study

Background

The utility of Cardiopulmonary Exercise Testing (CPET) to identify higher risk surgical patients remains controversial. There is limited research investigating the value of preoperative CPET to plan perioperative pathways for patients undergoing major pancreatic surgery.

Methods

Retrospective cohort study, comprising two groups before and after a change in referral policy for High Risk preoperative anaesthetic clinic with CPET. Period 1 discretionary referral and Period 2: universal referral. The primary aim was to investigate the impact of this policy change on critical care use (planned vs unplanned) on the day of surgery and on delayed critical care admission. Secondary end points included a comparison of the total number of critical care bed days, days in hospital, complication rates and mortality data between the two cohorts.

Results

177 patients were included; 114 in Period 1 and 63 in Period 2. There was a reduction in unplanned day of surgery postoperative admissions to critical care (28.1% vs. 11.1%, p = 0.008). Seven (6.1%) of patients in Period 1 and 1 (1.6%) patient in Period 2 had delayed admission, though no p value was calculated due to the small numbers involved. Complication rates were similar in each group. The median critical care bed days was 1 (range 0-21) days in Period 1 and 1 (0-13) days in Period 2.

Conclusions

A universal referral policy for preoperative CPET demonstrated a decrease in unplanned day of surgery critical care admissions and a trend towards reducing delayed (>24 h postop) critical care admission which could be investigated in a larger study. No measurable impact was seen on clinical outcomes.

Cost-Effectiveness in Perioperative Care: Application of Markov Modeling to Pathways of Perioperative Care

OBJECTIVES

This study aimed to evaluate the application of cost-effectiveness modeling to redesign of perioperative care pathways, from a hospital perspective.

METHODS

A Markov cost-effectiveness model of patient transition between care locations, each with different characteristics and cost, was developed. Inputs were derived from clinical trials piloting a preoperative call center and a postoperative medium-acuity care unit. The effect chosen was days at home (DAH) after surgery, reflecting quality of in-hospital care, acknowledged financially by fundholders, and relevant to consumers. Cost was from the hospital's perspective. A model cycle time of 4 hours for 30 days reflected relevant timelines and costs.

RESULTS

A Markov model was successfully created, accounting for the care locations in the 2 pathways as model states and accounting for consequences and costs. Cost-effectiveness analysis allowed the calculation of an incremental cost-effectiveness ratio comparing these pathways, providing a mean incremental cost-effectiveness ratio of -$427 per additional DAH, where incremental costs and DAH were -$644 and +1.51, respectively. Probabilistic sensitivity analysis suggested the new pathway had a 61% probability of reduced costs and a 74% probability of increased DAH and a 58% probability this pathway was dominant. Tornado analysis revealed the major contributor to increased costs as intensive care unit stay and the major contributor to decreased costs as ward stay. For the new pathway, the probability of transfer from ward to home and the probability of staying at home had the greatest impact on DAH.

CONCLUSIONS

These data suggest Markov modeling may be a useful tool for the cost-effectiveness analysis of initiatives in perioperative care.

Increased adherence to perioperative safety guidelines associated with improved patient safety outcomes: a stepped-wedge, cluster-randomised multicentre trial

BACKGROUND

National Dutch guidelines have been introduced to improve suboptimal perioperative care. A multifaceted implementation programme (IMPlementatie Richtlijnen Operatieve VEiligheid [IMPROVE]) has been developed to support hospitals in applying these guidelines. This study evaluated the effectiveness of IMPROVE on guideline adherence and the association between guideline adherence and patient safety.

METHODS

Nine hospitals participated in this unblinded, superiority, stepped-wedge, cluster RCT in patients with major noncardiac surgery (mortality risk ≥1%). IMPROVE consisted of educational activities, audit and feedback, reminders, organisational, team-directed, and patient-mediated activities. The primary outcome of the study was guideline adherence measured by nine patient safety indicators on the process (stop moments from the composite STOP bundle, and timely administration of antibiotics) and on the structure of perioperative care. Secondary safety outcomes included in-hospital complications, postoperative wound infections, mortality, length of hospital stay, and unplanned care.

RESULTS

Data were analysed for 1934 patients. The IMPROVE programme improved one stop moment: 'discharge from recovery room' (+16%; 95% confidence interval [CI], 9-23%). This stop moment was related to decreased mortality (-3%; 95% CI, -4% to -1%), fewer complications (-8%; 95% CI, -13% to -3%), and fewer unscheduled transfers to the ICU (-6%; 95% CI, -9% to -3%). IMPROVE negatively affected one other stop moment - 'discharge from the hospital' - possibly because of the limited resources of hospitals to improve all stop moments together.

CONCLUSIONS

Mixed implementation effects of IMPROVE were found. We found some positive associations between guideline adherence and patient safety (i.e. mortality, complications, and unscheduled transfers to the ICU) except for the timely administration of antibiotics.

CLINICAL TRIAL REGISTRATION

NTR3568 (Dutch Trial Registry).

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.

Systematic review of perioperative mortality risk prediction models for adults undergoing inpatient non-cardiac surgery

BACKGROUND

Risk prediction tools can be used in the perioperative setting to identify high-risk patients who may benefit from increased surveillance and monitoring in the postoperative period, to aid shared decision-making, and to benchmark risk-adjusted hospital performance. We evaluated perioperative risk prediction tools relevant to an Australian context.

METHODS

A systematic review of perioperative mortality risk prediction tools used for adults undergoing inpatient noncardiac surgery, published between 2011 and 2019 (following an earlier systematic review). We searched Medline via OVID using medical subject headings consistent with the three main areas of risk, surgery and mortality/morbidity. A similar search was conducted in Embase. Tools predicting morbidity but not mortality were excluded, as were those predicting a composite outcome that did not report predictive performance for mortality separately. Tools were also excluded if they were specifically designed for use in cardiac or other highly specialized surgery, emergency surgery, paediatrics or elderly patients.

RESULTS

Literature search identified 2568 studies for screening, of which 19 studies identified 21 risk prediction tools for inclusion.

CONCLUSION

Four tools are candidates for adapting in the Australian context, including the Surgical Mortality Probability Model (SMPM), Preoperative Score to Predict Postoperative Mortality (POSPOM), Surgical Outcome Risk Tool (SORT) and NZRISK. SORT has similar predictive performance to POSPOM, using only six variables instead of 17, contains all variables of the SMPM, and the original model developed in the UK has already been successfully adapted in New Zealand as NZRISK. Collecting the SORT and NZRISK variables in a national surgical outcomes study in Australia would present an opportunity to simultaneously investigate three of our four shortlisted models and to develop a locally valid perioperative mortality risk prediction model with high predictive performance.

'Organisation of delivery of care in operating suite recovery rooms within 48 hours postoperatively and patient outcomes after adult non-cardiac surgery: a systematic review'

CONTEXT

Postoperative recovery rooms have existed since 1847, however, there is sparse literature investigating interventions undertaken in recovery, and their impact on patients after recovery room discharge.

OBJECTIVE

This review aimed to investigate the organisation of care delivery in postoperative recovery rooms; and its effect on patient outcomes; including mortality, morbidity, unplanned intensive care unit (ICU) admission and length of hospital stay.

DATA SOURCES

NCBI PubMed, EMBASE and Cumulative Index to Nursing and Allied Health Literature.

STUDY SELECTION

Studies published since 1990, investigating health system initiatives undertaken in postoperative recovery rooms. One author screened titles and abstracts, with two authors completing full-text reviews to determine inclusion based on predetermined criteria. A total of 3288 unique studies were identified, with 14 selected for full-text reviews, and 8 included in the review.

DATA EXTRACTION

EndNote V.8 (Clarivate Analytics) was used to manage references. One author extracted data from each study using a data extraction form adapted from the Cochrane Data Extraction Template, with all data checked by a second author.

DATA SYNTHESIS

Narrative synthesis of data was the primary outcome measure, with all data of individual studies also presented in the summary results table.

RESULTS

Four studies investigated the use of the postanaesthesia care unit (PACU) as a non-ICU pathway for postoperative patients. Two investigated the implementation of physiotherapy in PACU, one evaluated the use of a new nursing scoring tool for detecting patient deterioration, and one evaluated the implementation of a two-track clinical pathway in PACU.

CONCLUSIONS

Managing selected postoperative patients in a PACU, instead of ICU, does not appear to be associated with worse patient outcomes, however, due to the high risk of bias within studies, the strength of evidence is only moderate. Four of eight studies also examined hospital length of stay; two found the intervention was associated with decreased length of stay and two found no association.

PROSPERO REGISTRATION NUMBER

This protocol is registered on the International Prospective Register of Systematic Reviews (PROSPERO) database, registration number CRD42018106093.

Cardiopulmonary Exercise Testing for Preoperative Evaluation: What Does the Future Hold?

Purpose of Review

Cardiopulmonary exercise testing (CPET) informs the preoperative evaluation process by providing individualised risk profiles; guiding shared decision-making, comorbidity optimisation and preoperative exercise training; and informing perioperative patient management. This review summarises evidence on the role of CPET in preoperative evaluation and explores the role of novel and emerging CPET variables and alternative testing protocols that may improve the precision of preoperative evaluation in the future.

Recent Findings

CPET provides a wealth of physiological data, and to date, much of this is underutilised clinically. For example, impaired chronotropic responses during and after CPET are simple to measure and in recent studies are predictive of both cardiac and noncardiac morbidity following surgery but are rarely reported. Exercise interventions are increasingly being used preoperatively, and endurance time derived from a high intensity constant work rate test should be considered as the most sensitive method of evaluating the response to training. Further research is required to identify the clinically meaningful difference in endurance time. Measuring efficiency may have utility, but this requires exploration in prospective studies.

Summary

Further work is needed to define contemporaneous risk thresholds, to explore the role of other CPET variables in risk prediction, to better characterise CPET’s role in combination with other tools in multifactorial risk stratification and increasingly to evaluate CPET’s utility for preoperative exercise prescription in prehabilitation.

Evaluation of the Collaborative Use of an Evidence-Based Care Bundle in Emergency Laparotomy

Question

Is a quality improvement collaborative approach to implementation of a care bundle associated with reductions in mortality from emergency laparotomy?

Findings

In this study of a collaborative project involving 28 hospitals and a total of 14 809 patients, reductions in mortality and length of stay were seen after implementation of a care bundle. Improvement took time to occur and was not seen until the second year of the collaborative project.

Meaning

The findings suggest that hospitals should consider adopting a care bundle approach and participating in a collaborative group to see improvement in outcomes for patients undergoing emergency laparotomy.

Importance

Patients undergoing emergency laparotomy have high mortality, but few studies exist to improve outcomes for these patients.

Objective

To assess whether a collaborative approach to implement a 6-point care bundle is associated with reduction in mortality and length of stay and improvement in the delivery of standards of care across a group of hospitals.

Design, Setting, and Participants

The Emergency Laparotomy Collaborative (ELC) was a UK-based prospective quality improvement study of the implementation of a care bundle provided to patients requiring emergency laparotomy between October 1, 2015, and September 30, 2017. Participants were 28 National Health Service hospitals and emergency surgical patients who were treated at these hospitals and whose data were entered into the National Emergency Laparotomy Audit (NELA) database. Post-ELC implementation outcomes were compared with baseline data from July 1, 2014, to September 30, 2015. Data entry and collection were performed through the NELA.

Interventions

A 6-point, evidence-based care bundle was used. The bundle included prompt measurement of blood lactate levels, early review and treatment for sepsis, transfer to the operating room within defined time goals after the decision to operate, use of goal-directed fluid therapy, postoperative admission to an intensive care unit, and multidisciplinary involvement of senior clinicians in the decision and delivery of perioperative care. Change management and leadership coaching were provided to ELC leadership teams.

Main Outcome and Measures

Primary outcomes were in-hospital mortality, both crude and Portsmouth Physiological and Operative Severity Score for the enumeration of Mortality and morbidity (P-POSSUM) risk-adjusted, and length of stay. Secondary outcomes were the changes after implementation of the separate metrics in the care bundle.

Results

A total of 28 hospitals participated in the ELC and completed the project. The baseline group included 5562 patients (2937 female [52.8%] and a mean [range] age of 65.3 [18.0-114.0] years), whereas the post-ELC group had 9247 patients (4911 female [53.1%] and a mean [range] age of 65.0 [18.0-99.0] years). Unadjusted mortality rate decreased from 9.8% at baseline to 8.3% in year 2 of the project, and so did risk-adjusted mortality from a baseline of 5.3% to 4.5% post-ELC. Mean length of stay decreased from 20.1 days during year 1 to 18.9 days during year 2. Significant changes in 5 of the 6 metrics in the care bundle were achieved.

Conclusions and Relevance

A collaborative approach using a quality improvement methodology and a care bundle appeared to be effective in reducing mortality and length of stay in emergency laparotomy, suggesting that hospitals should adopt such an approach to see better patient outcomes and care delivery performance.

Postoperative critical care and high-acuity care provision in the United Kingdom, Australia, and New Zealand

BACKGROUND

Decisions to admit high-risk postoperative patients to critical care may be affected by resource availability. We aimed to quantify adult ICU/high-dependency unit (ICU/HDU) capacity in hospitals from the UK, Australia, and New Zealand (NZ), and to identify and describe additional 'high-acuity' beds capable of managing high-risk patients outside the ICU/HDU environment.

METHODS

We used a modified Delphi consensus method to design a survey that was disseminated via investigator networks in the UK, Australia, and NZ. Hospital- and ward-level data were collected, including bed numbers, tertiary services offered, presence of an emergency department, ward staffing levels, and the availability of critical care facilities.

RESULTS

We received responses from 257 UK (response rate: 97.7%), 35 Australian (response rate: 32.7%), and 17 NZ (response rate: 94.4%) hospitals (total 309). Of these hospitals, 91.6% reported on-site ICU or HDU facilities. UK hospitals reported fewer critical care beds per 100 hospital beds (median=2.7) compared with Australia (median=3.7) and NZ (median=3.5). Additionally, 31.1% of hospitals reported having high-acuity beds to which high-risk patients were admitted for postoperative management, in addition to standard ICU/HDU facilities. The estimated numbers of critical care beds per 100 000 population were 9.3, 14.1, and 9.1 in the UK, Australia, and NZ, respectively. The estimated per capita high-acuity bed capacities per 100 000 population were 1.2, 3.8, and 6.4 in the UK, Australia, and NZ, respectively.

CONCLUSIONS

Postoperative critical care resources differ in the UK, Australia, and NZ. High-acuity beds may have developed to augment the capacity to deliver postoperative critical care.

The cardiopulmonary exercise test grey zone; optimising fitness stratification by application of critical difference

BACKGROUND

Cardiorespiratory fitness can inform patient care, although to what extent natural variation in CRF influences clinical practice remains to be established. We calculated natural variation for cardiopulmonary exercise test (CPET) metrics, which may have implications for fitness stratification.

METHODS

In a two-armed experiment, critical difference comprising analytical imprecision and biological variation was calculated for cardiorespiratory fitness and thus defined the magnitude of change required to claim a clinically meaningful change. This metric was retrospectively applied to 213 patients scheduled for colorectal surgery. These patients underwent CPET and the potential for misclassification of fitness was calculated. We created a model with boundaries inclusive of natural variation [critical difference applied to oxygen uptake at anaerobic threshold (V˙O2-AT): 11 ml O2 kg-1 min-1, peak oxygen uptake (V˙O2 peak): 16 ml O2 kg-1 min-1, and ventilatory equivalent for carbon dioxide at AT (V̇E/V̇CO2-AT): 36].

RESULTS

The critical difference for V˙O2-AT, V˙O2 peak, and V˙E/V˙CO2-AT was 19%, 13%, and 10%, respectively, resulting in false negative and false positive rates of up to 28% and 32% for unfit patients. Our model identified boundaries for unfit and fit patients: AT <9.2 and ≥13.6 ml O2 kg-1 min-1, V˙O2 peak <14.2 and ≥18.3 ml kg-1 min-1, V˙E/V˙CO2-AT ≥40.1 and <32.7, between which an area of indeterminate-fitness was established. With natural variation considered, up to 60% of patients presented with indeterminate-fitness.

CONCLUSIONS

These findings support a reappraisal of current clinical interpretation of cardiorespiratory fitness highlighting the potential for incorrect fitness stratification when natural variation is not accounted for.

[CR-POSSUM and Surgical Apgar Score as predictive factors for patients' allocation after colorectal surgery]

BACKGROUND AND OBJECTIVES

Surgical patients frequently require admission in high-dependency units or intensive care units. Resources are scarce and there are no universally accepted admission criteria, so patients' allocation must be optimized. The purpose of this study was to investigate the relationship between postoperative destination of patients submitted to colorectal surgery and the scores ColoRectal Physiological and Operative Severity Score for the enUmeration of Mortality and Morbidity (CR-POSSUM) and Surgical Apgar Score (SAS) and, secondarily find cut-offs to aid this allocation.

METHODS

A cross-sectional prospective observational study, including all adult patients undergoing colorectal surgery during a 2 years period. Data collected from the electronic clinical process and anesthesia records.

RESULTS

A total of 358 patients were included. Median score for SAS was 8 and CR-POSSUM had a median mortality probability of 4.5%. Immediate admission on high-dependency units/intensive care units occurred in 51 patients and late admission in 18. Scores from ward and high-dependency units/intensive care units patients were statistically different (SAS: 8 vs. 7, p<0.001; CR-POSSUM: 4.4% vs. 15.9%, p<0.001). Both scores were found to be predictors of immediate postoperative destination (p<0.001). Concerning immediate high-dependency units/intensive care units admission, CR-POSSUM showed a strong association (AUC 0.78, p=0.034) with a ≥9.16 cut-off point (sensitivity: 62.5%; specificity: 75.2%), outperforming SAS (AUC 0.67, p=0.048), with a ≤7 cut-off point (sensitivity: 67.3%; specificity: 56.1%).

CONCLUSIONS

Both CR-POSSUM and SAS were associated with the clinical decision to admit a patient to the high-dependency units/intensive care units immediately after surgery. CR-POSSUM alone showed a better discriminative capacity.

Risk Assessment

Purpose of Review

The central question of preoperative assessment is not “What can be done?” but “What should be done and how?” Predicting a patient’s risk of unwanted outcomes is vital to answering this question. This review discusses risk prediction tools currently available and anticipates future developments.

Recent Findings

Simple, parsimonious risk scales and scores are being replaced by complex risk prediction models as high-capacity information systems become ubiquitous. The accuracy of risk estimation will be further increased by improved assessment of physical fitness, frailty, and incorporation of existing and novel biomarkers. However, the limitations of risk prediction for individual patient care must be recognized.

Summary

Risk prediction is transforming from clinical estimation to statistical science. Predictions should be used within the context of a patient’s baseline risk (life expectancy independent of surgery), personal circumstances, quality of life, their expectations and values, and consideration of outcomes that are meaningful for the patient.

Perioperative risk

The high-risk surgical patient is a growing challenge to modern health care. This cohort, although comprising only 10-15% of surgical procedures, accounts for approximately 80% of postoperative deaths and suffers a high rate of postoperative morbidity. Developing robust systems to help identify and better manage this patient group should be a priority. Risk stratification has become a valuable clinical tool for shared decision-making and the development of individualized care plans. Methods for stratifying individual risk include assessment tools, measures of functional capacity and plasma biomarker assays. Routine evaluation of perioperative risk is central to the delivery of high quality, appropriate surgical care.