Vasopressors to treat postoperative hypotension after adult noncardiac, non-obstetric surgery: a systematic review.

BACKGROUND: Postoperative hypotension is common after major surgery and is associated with patient harm. Vasopressors are commonly used to treat hypotension without clear evidence of benefit. We conducted a systematic review to better understand the use, impact, and rationale for vasopressor administration after noncardiac, non-obstetric surgery in adults. METHODS: We conducted a prospectively registered systematic review. Cochrane CENTRAL, EMBASE, MEDBASE, and MEDLINE were searched for RCTs and cohort studies of adult patients receiving vasopressors after noncardiac, non-obstetric surgery. Study quality was critically appraised by two investigators. Findings from the review were synthesised, but formal meta-analysis was not performed because of significant variability in study populations and outcomes. RESULTS: A total of 3201 articles were screened, of which seven RCTs, two prospective cohort studies, and 15 retrospective cohort studies were included in the analysis (24 in total). One study was graded as high quality, two as moderate quality, and the remaining 21 as low quality. Sixteen studies relied on clinical assessment alone to decide on therapeutic interventions. Vasodilation was the most common suggested physiological disturbance. The median proportion of patients receiving vasopressors was 42% (interquartile range: 11.5-74.7%). Norepinephrine was the most common vasopressor used. CONCLUSIONS: The evidence supporting the use of vasopressors to treat postoperative hypotension is limited. Future research should focus on whether vasodilatation or other physiological disturbance is driving postoperative hypotension to allow rational decision-making.

Research Trends in Anticoagulation Therapy over the Last 25 Years.

A large volume of literature has become available to practitioners prescribing anticoagulants. The aim of this study was to analyze the bibliometric characteristics of the top 100 most cited articles related to anticoagulation over the past 25 years, with special consideration to impact of direct or "nonvitamin K antagonist" oral anticoagulants (NOACs) compared with vitamin K antagonists. A bibliometric analysis of the 100 most cited journal articles related to anticoagulants published between 1994 and 2019 was performed in April 2019. The top 100 articles by citation count were analyzed to extract bibliometric data related to journal title, impact factor, year of publication, place of publication, anticoagulant studied, indication for anticoagulation, study design, and conflicts of interest. The median (interquartile range) number of citations per article was 806 (621-1,085). The anticoagulant most frequently researched was warfarin (37%). NOAC publications (21%) grew at a relative rate of 3.4 times faster compared with all publications. The indication most commonly researched was venous thromboembolism (26%). Eighty articles constituted level I or II evidence, with randomized controlled trials the most common type of study (74). A financial conflict of interest was declared in 87% of articles with private, for-profit organizations the most common source of funding (26%). In summary, top research related to anticoagulation is highly impactful but may be at risk of sponsorship bias. High-level evidence for NOACs continues to expand across a range of indications with citation metrics likely to soon approach or surpass that of older drugs.

Frailty and outcomes from pneumonia in critical illness: a population-based cohort study.

BACKGROUND: A threshold Clinical Frailty Scale (CFS) of 5 (indicating mild frailty) has been proposed to guide ICU admission for UK patients with coronavirus disease 2019 (COVID-19) pneumonia. However, the impact of frailty on mortality with (non-COVID-19) pneumonia in critical illness is unknown. We examined the triage utility of the CFS in patients with pneumonia requiring ICU. METHODS: We conducted a retrospective cohort study of adult patients admitted with pneumonia to 170 ICUs in Australia and New Zealand from January 1, 2018 to September 31, 2019. We classified patients as: non-frail (CFS 1-4) frail (CFS 5-8), mild/moderately frail (CFS 5-6),and severe/very severely frail (CFS 7-8). We evaluated mortality (primary outcome) adjusting for site, age, sex, mechanical ventilation, pneumonia type and illness severity. We also compared the proportion of ICU bed-days occupied between frailty categories. RESULTS: 1852/5607 (33%) patients were classified as frail, including1291/3056 (42%) of patients aged >65 yr, who would potentially be excluded from ICU admission under UK-based COVID-19 triage guidelines. Only severe/very severe frailty scores were associated with mortality (adjusted odds ratio [aOR] for CFS=7: 3.2; 95% confidence interval [CI]: 1.3-7.8; CFS=8 [aOR: 7.2; 95% CI: 2.6-20.0]). These patients accounted for 7% of ICU bed days. Vulnerability (CFS=4) and mild frailty (CFS=5) were associated with a similar mortality risk (CFS=4 [OR: 1.6; 95% CI: 0.7-3.8]; CFS=5 [OR: 1.6; 95% CI: 0.7-3.9]). CONCLUSIONS: Patients with severe and very severe frailty account for relatively few ICU bed days as a result of pneumonia, whilst adjusted mortality analysis indicated little difference in risk between patients in vulnerable, mild, and moderate frailty categories. These data do not support CFS ≥5 to guide ICU admission for pneumonia.

Accuracy of the Clinical Frailty Scale for perioperative frailty screening: a prospective observational study.

PURPOSE: Perioperative frailty increases postoperative complications, mortality, and new functional dependence. Despite this, routine perioperative frailty screening is not widespread. We aimed to assess the accuracy of the Clinical Frailty Scale (CFS) as a screening tool prior to anesthesia, and to determine which health domains are affected by frailty. METHODS: In a prospective, single-centre observational study, we enrolled 218 patients aged ≥ 65 yr undergoing elective and emergency surgery. The screening performance of the CFS was compared with the Edmonton Frail Scale, including the effect in individual frailty domains, and outcomes including discharge location and mortality. RESULTS: The median [interquartile range] age of the enrolled subjects was 74 [69-80] yr and 24% of the patients were frail. The CFS and Edmonton scales were highly correlated (Spearman correlation coefficient, 0.81; 95% confidence interval [CI], 0.77 to 0.86), and in substantial agreement (kappa coefficient, 0.76; 95% CI, 0.70 to 0.81), with an area under the receiver operating characteristic curve of 0.91 (95% CI, 0.86 to 0.94) indicating excellent discrimination for the CFS in predicting frailty status based on the Edmonton scale. Frail patients had higher 30-day mortality (odds ratio, 5.26; 95% CI, 1.28 to 21.62), and were less likely to be discharged home. Frail patients had poorer health throughout frailty domains, including functional dependence (42% of frail vs 4% of non-frail patients; P < 0.001), malnutrition (48% vs 19%, P < 0.001), and poor physical performance (47% vs 7%, P < 0.001). CONCLUSION: The CFS is a valid and accurate tool to screen for perioperative frailty, which encompasses the spectrum of health-related domains.

RéSUMé: OBJECTIF: La fragilité périopératoire augmente les complications postopératoires, la mortalité et une nouvelle dépendance fonctionnelle. Le dépistage de routine de la fragilité périopératoire n'est cependant pas une pratique répandue. Nous avions pour objectif d'évaluer la précision de l'échelle de mesure de fragilité CFS (pour Clinical Frailty Scale) comme outil de dépistage préanesthésique et de déterminer quels domaines de la santé étaient affectés par la fragilité. MéTHODE: Nous avons recruté 218 patients âgés de plus de 65 ans et subissant une chirurgie non urgente ou urgente dans notre étude observationnelle prospective et monocentrique. Les résultats du dépistage de la CFS ont été comparés à l'échelle de fragilité d'Edmonton (Edmonton Frail Scale), y compris en ce qui a trait à l'effet de la fragilité sur les domaines individuels de fragilité et aux résultats tels que la destination au congé et la mortalité. RéSULTATS: L'âge médian [écart interquartile] des patients recrutés était de 74 [69­80] ans et 24 % des patients étaient fragiles. Les échelles CFS et d'Edmonton avaient une forte corrélation (coefficient de corrélation de Spearman, 0,81; intervalle de confiance [IC] 95 %, 0,77 à 0,86) et étaient en accord substantiel (coefficient kappa, 0,76; IC 95 %, 0,70 à 0,81), avec une surface sous la courbe de fonction d'efficacité de l'observateur de 0,91 (IC 95 %, 0,86 à 0,94), indiquant une discrimination excellente de la CFS pour prédire l'état de fragilité fondé sur l'échelle d'Edmonton. Les patients fragiles souffraient d'une mortalité à 30 jours plus élevée (rapport de cotes, 5,26; IC 95 %, 1,28 à 21,62) et il était moins probable qu'ils reçoivent leur congé de l'hôpital à la maison. Les patients fragiles étaient en moins bonne santé dans tous les domaines de fragilité, notamment en dépendance fonctionnelle (42 % des patients fragiles vs 4 % des patients non fragiles; P < 0,001), en malnutrition (48 % vs 19 %, P < 0,001) et en mauvaise performance physique (47 % vs 7 %, P < 0,001). CONCLUSION: L'échelle CFS constitue un outil valable et précis pour dépister la fragilité périopératoire, qui englobe l'éventail des domaines liés à la santé.

Frailty in very old critically ill patients in Australia and New Zealand: a population-based cohort study.

OBJECTIVE: To explore associations between frailty (Clinical Frailty Scale score of 5 or more) in very old patients in intensive care units (ICUs) and their clinical outcomes (mortality, discharge destination). DESIGN, SETTING AND PARTICIPANTS: Retrospective population cohort analysis of Australian and New Zealand Intensive Care Society (ANZICS) Adult Patient Database data for all patients aged 80 years or more admitted to participating ICUs between 1 January 2017 and 31 December 2018. MAIN OUTCOME MEASURES: Primary outcome: in-hospital mortality; secondary outcomes: length of stay (hospital, ICU), re-admission to ICU during the same hospital admission, discharge destination (including new chronic care or nursing home admission). RESULTS: Frailty status data were available for 15 613 of 45 773 patients aged 80 years or more admitted to 178 ICUs (34%); 6203 of these patients (39.7%) were deemed frail. A smaller proportion of frail than non-frail patients were men (47% v 57%), the mean illness severity scores of frail patients were slightly higher than those of non-frail patients, and they were more frequently admitted from the emergency department (28% v 21%) or with sepsis (12% v 7%) or respiratory complications (16% v 12%). In-hospital mortality was higher for frail patients (17.6% v 8.2%; adjusted odds ratio [OR], 1.87 [95% CI, 1.65-2.11]). Median lengths of ICU and hospital stay were slightly longer for frail patients, and they were more frequently discharged to new nursing home or chronic care (4.9% v 2.8%; adjusted OR, 1.61 [95% CI, 1.34-1.95]). CONCLUSIONS: Many very old critically ill patients in Australia and New Zealand are frail, and frailty is associated with considerably poorer health outcomes. Routine screening of older ICU patients for frailty could improve outcome prediction and inform intensive care and community health care planning.

Anaesthetist prediction of postoperative opioid use: a multicentre prospective cohort study.

Background: The Apfel simplified risk score includes four risk factors: female sex, non-smoking status, postoperative nausea and vomiting or motion sickness history, and postoperative opioid use. The score is calculated preoperatively, so postoperative opioid use must be predicted. We aimed to determine whether anaesthetists can predict patients' postoperative opioid use and dose. Methods: Specialist anaesthetists from eight hospitals preoperatively predicted opioid use and dose in the post-anaesthesia care unit (PACU) and for the first 24 h postoperatively, which was compared with actual opioid use and dose. Opioid doses were converted to oral morphine equivalents (MEQ). Correlations between predicted and actual opioid use and dose were analysed with Spearman's rho and linear regression. Results: A total of 487 anaesthetist-patient pairs were included. Anaesthetists overpredicted opioid use (398 [82%] predicted vs 251 [52%] actual patients requiring opioids in the PACU; 396 [81%] predicted vs 291 [60%] actual in the first 24 h) (Spearman's rho [95% confidence interval] 0.24 [0.16-0.33], P<0.001 in the PACU; 0.36 [0.28-0.44], P<0.001 in the first 24 h). Anaesthetists also overpredicted opioid dose (median [inter-quartile range] 12 [8-20] mg predicted MEQ vs 4 [0-18] mg actual MEQ in the PACU; 32 [18-60] mg vs 24 [0-65] mg MEQ in the first 24 h) (Spearman's rho 0.21 [0.13-0.29], P<0.001 in the PACU; 0.53 [0.40-0.60], P<0.001 in the first 24 h). Conclusions: Specialist anaesthetists cannot accurately predict opioid use or dose in the PACU or the first 24 postoperative hours. The Apfel risk criterion for postoperative opioid use may be inaccurate in clinical practice.

The Clinical Frailty Scale for mortality prediction of old acutely admitted intensive care patients: a meta-analysis of individual patient-level data.

BACKGROUND: This large-scale analysis pools individual data about the Clinical Frailty Scale (CFS) to predict outcome in the intensive care unit (ICU). METHODS: A systematic search identified all clinical trials that used the CFS in the ICU (PubMed searched until 24th June 2020). All patients who were electively admitted were excluded. The primary outcome was ICU mortality. Regression models were estimated on the complete data set, and for missing data, multiple imputations were utilised. Cox models were adjusted for age, sex, and illness acuity score (SOFA, SAPS II or APACHE II). RESULTS: 12 studies from 30 countries with anonymised individualised patient data were included (n = 23,989 patients). In the univariate analysis for all patients, being frail (CFS ≥ 5) was associated with an increased risk of ICU mortality, but not after adjustment. In older patients (≥ 65 years) there was an independent association with ICU mortality both in the complete case analysis (HR 1.34 (95% CI 1.25-1.44), p < 0.0001) and in the multiple imputation analysis (HR 1.35 (95% CI 1.26-1.45), p < 0.0001, adjusted for SOFA). In older patients, being vulnerable (CFS 4) alone did not significantly differ from being frail. After adjustment, a CFS of 4-5, 6, and ≥ 7 was associated with a significantly worse outcome compared to CFS of 1-3. CONCLUSIONS: Being frail is associated with a significantly increased risk for ICU mortality in older patients, while being vulnerable alone did not significantly differ. New Frailty categories might reflect its "continuum" better and predict ICU outcome more accurately. TRIAL REGISTRATION: Open Science Framework (OSF: https://osf.io/8buwk/ ).

Impact of frailty on persistent critical illness: a population-based cohort study.

PURPOSE: Acute illness severity predicts mortality in intensive care unit (ICU) patients, however, its predictive value decreases over time in ICU. Typically after 10 days, pre-ICU (antecedent) characteristics become more predictive of mortality, defining the onset of persistent critical illness (PerCI). How patient frailty affects development and death from PerCI is unknown. METHODS: We conducted a secondary analysis of data from a prospective binational cohort study including 269,785 critically ill adults from 168 ICUs in Australia and New Zealand, investigating whether frailty measured with the Clinical Frailty Scale (CFS) changes the timing of onset and risk of developing PerCI and of subsequent in-hospital mortality. We assessed associations between frailty (CFS ≥ 5) and mortality prediction using logistic regression and area under the receiver operating characteristics (AUROC) curves. RESULTS: 2190 of 50,814 (4.3%) patients with frailty (CFS ≥ 5) versus 6624 of 218,971 (3%) patients without frailty (CFS ≤ 4) developed PerCI (P < 0.001). Among patients with PerCI, 669 of 2190 (30.5%) with frailty and 1194 of 6624 without frailty (18%) died in hospital (P < 0.001). The time point defining PerCI onset did not vary with frailty degree; however, with increasing length of ICU stay, inclusion of frailty progressively improved mortality discrimination (0.1% AUROC improvement on ICU day one versus 3.6% on ICU day 17). CONCLUSION: Compared to patients without frailty, those with frailty have a higher chance of developing and dying from PerCI. Moreover the importance of frailty as a predictor of mortality increases with ICU length of stay. Future work should explore incorporation of frailty in prognostic models, particularly for long-staying patients.

The Relationship between Frailty and Mechanical Ventilation: A Population-based Cohort Study.

Rationale: Frailty in critically ill patients is associated with higher mortality and prolonged length of stay; however, little is known about the impact on the duration of mechanical ventilation. Objectives: To identify the relationship between frailty and total duration of mechanical ventilation and the interaction with patients' age. Methods: This retrospective population-based cohort study was performed using data submitted to the Australian and New Zealand Intensive Care Society Adult Patient Database between 2017 and 2020. We analyzed adult critically ill patients who received invasive mechanical ventilation within the first 24 hours of intensive care unit admission. Results: Of 59,319 available patients receiving invasive mechanical ventilation, 8,331 (14%) were classified as frail. Patients with frailty had longer duration of mechanical ventilation compared with patients without frailty. Duration of mechanical ventilation increased with higher frailty score. Patients with frailty had longer intensive care unit and hospital stay with higher mortality than patients without frailty. After adjustment for relevant covariates in multivariate analyses, frailty was significantly associated with a reduced probability of cessation of invasive mechanical ventilation (adjusted hazard ratio, 0.57 [95% confidence interval, 0.51-0.64]; P < 0.001). Sensitivity and subgroup analyses suggested that frailty could prolong mechanical ventilation in survivors, and the relationship was especially strong in younger patients. Conclusions: Frailty score was independently associated with longer duration of mechanical ventilation and contributed to identifying patients who were less likely to be liberated from mechanical ventilation. The impact of frailty on ventilation time varied with age and was most apparent for younger patients.

Long-term Survival of Critically Ill Patients Stratified According to Pandemic Triage Categories: A Retrospective Cohort Study.

BACKGROUND: The COVID-19 pandemic has led to unprecedented demand for ICUs, with the need to triage admissions along with the development of ICU triage criteria. However, how these criteria relate to outcomes in patients already admitted to the ICU is unknown, as is the incremental ICU capacity that triage of these patients might create given existing admission practices. RESEARCH QUESTION: What is the short- and long-term survival of low- vs high-priority patients for ICU admission according to current pandemic triage criteria? STUDY DESIGN AND METHODS: This study analyzed prospectively collected registry data (2007-2018) in 23 ICUs in Victoria, Australia, with probabilistic linkage with death registries. After excluding elective surgery, admissions were stratified according to existing ICU triage protocol prioritization as low (age ≥ 85 years, or severe chronic illness, or Sequential Organ Failure Assessment [SOFA] score = 0 or ≥ 12), medium (SOFA score = 8-11) or high (SOFA score = 1-7) priority. The primary outcome was long-term survival. Secondary outcomes were in-hospital mortality, ICU length of stay (LOS) and bed-day usage. RESULTS: This study examined 126,687 ICU admissions. After 5 years of follow-up, 1,093 of 3,296 (33%; 95% CI, 32-34) of "low-priority" patients aged ≥ 85 years or with severe chronic illness and 86 of 332 (26%; 95% CI, 24-28) with a SOFA score ≥ 12 were still alive. Sixty-three of 290 (22%; 95% CI, 17-27) of patients in these groups followed up for 10 years were still alive. Together, low-priority patients accounted for 27% of all ICU bed-days and had lower in-hospital mortality (22%) than the high-priority patients (28%). Among nonsurvivors, low-priority admissions had shorter ICU LOS than medium- or high-priority admissions. INTERPRETATION: Current SOFA score or age or severe comorbidity-based ICU pandemic triage protocols exclude patients with a close to 80% hospital survival, a > 30% five-year survival, and 27% of ICU bed-day use. These findings imply the need for stronger evidence-based ICU triage protocols.

The risk of postoperative deterioration of non-cardiac surgery patients with ICU referral status who are admitted to the regular ward: a retrospective observational cohort study.

BACKGROUND: Higher-risk surgical patients may not be admitted to the intensive care unit due to stable immediate post-operative status on review. The outcomes of this cohort are not well described. Our aim was to examine the subsequent inpatient course of intensive care unit -referred but not admitted surgical patients. METHODS: All patients aged ≥18 years who were referred but not admitted for post-operative management in a tertiary metropolitan intensive care unit following non-cardiac surgery between 1/7/2017 and 30/6/2018 were eligible for inclusion in this retrospective observational cohort study. Primary outcome was Medical Emergency Team activation. Secondary outcomes included unplanned intensive care unit admission; length of stay; and 30-day mortality. Risk of serious complications and predicted length of stay were calculated using the National Surgical Quality Improvement Program scoring tool. RESULTS: Fifteen of 60 patients (25%) had a MET-call following surgery, eight (13%) patients required unplanned intensive care unit admission, with median (IQR) time to Medical Emergency Team call 9 (6-13) hours. No patients died within 30-days. There was no significant difference between mean National Surgical Quality Improvement Program predicted and actual length of stay; after adjustment, National Surgical Quality Improvement Program predicted risk of serious complications was associated with unplanned intensive care unit admission (OR [95% CI] = 1.08 [1.00-1.16], p = 0.04), although not Medical Emergency Team calls. CONCLUSIONS: Post-operative deterioration occurs frequently, and early, in a cohort of high-risk surgical patients initially assessed as being safe for ward care. Changes to current triage models for post-operative intensive care unit admission may reduce the impact of complications in this high-risk group.

Routine Frailty Screening in Critical Illness: A Population-Based Cohort Study in Australia and New Zealand.

BACKGROUND: Frailty is associated with poor outcomes in critical illness. However, it is unclear whether frailty screening on admission to the ICU can be conducted routinely at the population level and whether it has prognostic importance. RESEARCH QUESTION: Can population-scale frailty screening with the Clinical Frailty Scale (CFS) be implemented for critically ill adults in Australia and New Zealand (ANZ) and can it identify patients at risk of negative outcomes? STUDY DESIGN AND METHODS: We conducted a binational prospective cohort study of critically ill adult patients admitted between July 1, 2018, and June 30, 2020, in 175 ICUs in ANZ. We classified frailty with the CFS on admission to the ICU. The primary outcome was in-hospital mortality; secondary outcomes were length of stay (LOS), discharge destination, complications (delirium, pressure injury), and duration of survival. RESULTS: We included 234,568 critically ill patients; 45,245 (19%) were diagnosed as living with frailty before ICU admission. Patients with vs without frailty had higher in-hospital mortality (16% vs 5%; P < .001), delirium (10% vs 4%; P < .001), longer LOS in the ICU and hospital, and increased new chronic care discharge (3% vs 1%; P < .001), with worse outcomes associated with increasing CFS category. Of patients with very severe frailty (CFS score, 8), 39% died in hospital vs 2% of very fit patients (CFS score, 1; multivariate categorical CFS score, 8 [reference, 1]; OR, 7.83 [95% CI, 6.39-9.59]; P < .001). After adjustment for illness severity, frailty remained highly significantly predictive of mortality, including among patients younger than 50 years, with improvement in the area under the receiver operating characteristic curve of the Acute Physiology and Chronic Health Evaluation III-j score to 0.882 (95% CI, 0.879-0.885) from 0.868 (95% CI, 0.866-0.871) with the addition of frailty (P < .001). INTERPRETATION: Large-scale population screening for frailty degree in critical illness was possible and prognostically important, with greater frailty (especially CFS score of ≥ 6) associated with worse outcomes, including among younger patients.

Sugammadex, neostigmine and postoperative pulmonary complications: an international randomised feasibility and pilot trial.

BACKGROUND: Sugammadex reduces residual neuromuscular blockade after anaesthesia, potentially preventing postoperative pulmonary complications. However, definitive evidence is lacking. We therefore conducted a feasibility and pilot trial for a large randomised controlled trial of sugammadex, neostigmine, and postoperative pulmonary complications. METHODS: Patients aged ≥40 years having elective or expedited abdominal or intrathoracic surgery were recruited in Australia and Hong Kong. Perioperative care was at the discretion of clinicians, except for the use of rocuronium and/or vecuronium for neuromuscular blockade and the randomised intervention (sugammadex or neostigmine) for reversal. Feasibility measurements included recruitment, crossover, acceptability, completeness, and workload. Trial coordinator feedback was systematically sought. Patient-reported quality of life was measured using the EQ-5D-5L score. The primary pilot outcome was the incidence of new pulmonary complications up to hospital discharge (or postoperative day 7 if still in hospital). RESULTS: Among 150 eligible patients, 120 consented to participate (recruitment rate 80%, 95% confidence interval [CI] 73 to 86%). The randomised intervention was administered without crossover to 115 of 117 patients who received reversal (98%, 95% CI 94 to 100%). The protocol was acceptable or highly acceptable to the anaesthetist in 108 of 116 cases (93%, 95% CI 87 to 97%; missing = 4). Four patients of the 120 patients were lost to follow-up at 3 months (3.3%, 95% CI 0.9 to 8.3%). Case report forms were complete at 3 months for all remaining patients. The median time to complete trial processes was 3.5 h (range 2.5-4.5 h). Trial coordinators reported no barriers to trial processes. Patients were aged 64 (standard deviation 11) years, 70 (58%) were male and 50 (42%) were female, and planned surgeries were thoracic (23 [19%]), upper abdominal (41 [34%]), and lower abdominal (56 [47%]). The primary outcome was observed in 5 (8.5%) of the 59 sugammadex patients and 5 (8.2%) of the 61 neostigmine patients (odds ratio 1.02, 95% CI 0.28 to 3.67). CONCLUSIONS: A large international randomised controlled trial of sugammadex, neostigmine and postoperative pulmonary complications in adult patients having abdominal and intrathoracic surgery, including collection of cost-effectiveness evidence for Health Technology Appraisal, is feasible. TRIAL REGISTRATION: Prospectively registered at the Australian and New Zealand Clinical Trials Registry ( ACTRN12620001313921 ) on December 7, 2020. www.anzctr.org.au/Trial/Registration/TrialReview.aspx?id=380645&isReview=true .

Chewing gum prophylaxis for postoperative nausea and vomiting in the intensive care unit: a pilot randomised controlled trial.

Objective: To test the effectiveness of chewing gum in the prophylaxis of postoperative nausea and vomiting (PONV) in patients admitted to the intensive care unit (ICU) after surgery. Design: Prospective, open label, pilot randomised controlled trial. Setting: Two metropolitan ICUs. Participants: Ninety postoperative adult patients admitted to the ICU. Intervention: Patients administered chewing gum, who chewed for at least 15 minutes every 4 hours, were compared with a control group, who were administered a 20 mL sip of water orally every 4 hours. Main outcome measures: The primary outcome was the number of patient-reported episodes of nausea in the first 24 hours after the operation. Secondary outcomes included vomiting or dry retching episodes, and duration and severity of nausea. Results: Forty-six patients were randomly allocated to chewing gum and 44 patients to water. There was no difference between groups in the number of patients with nausea (10 [22%] chewing gum v 12 [27%] control patients; P = 0.72), nausea episodes (22 episodes; [median, 0; IQR, 0-0] v 21 episodes [median, 0; IQR, 0-1] per patient in each group respectively), vomiting/retching (2 [4%] chewing gum v 6 [14%] control patients; P = 0.24), or duration/severity of nausea. Conclusion: Regular postoperative administration of chewing gum in a surgical ICU patient cohort did not reduce nausea, vomiting or retching. The prevalence of PONV is less than previously reported. Our findings can inform future studies of PONV prophylaxis in post-surgical ICU patients. Trial registration: Australian New Zealand Clinical Trial Registry No. ACTRN12617001185358.

Development of a Frailty Index from Routine Hospital Data in Perioperative and Critical Care.

BACKGROUND/OBJECTIVES: Frailty is common in surgical and intensive care unit (ICU) populations, yet it is not routinely measured. Frailty indices are able to quantify this condition across a range of health deficits. We aimed to develop a frailty index (FI) from routinely collected hospital data in a surgical and ICU population. DESIGN: Prospective observational single-center cohort study. SETTING: Tertiary referral metropolitan Australian hospital. PARTICIPANTS: A total of 336 individuals aged 65 and older undergoing surgery or aged 50 and older admitted to the ICU. MEASUREMENTS: Routine admission health data were used to derive an FI comprising 36 health deficits. We examined the FI correlation with existing frailty tools (Clinical Frailty Scale [CFS] and Edmonton Frail Scale [EFS]) and assessed its predictive ability for negative outcomes including 30-day mortality. RESULTS: Median FI was .17 (interquartile range [IQR]) = .10-.24) for ICU patients and .17 (IQR = .11-.25) for surgical patients; maximum FI was .58, and 25% (95% confidence interval [CI] = 10.4-29.6) of patients overall were diagnosed with frailty (FI score ≥.25). Correlation was strong between the FI and the EFS: ρ = .76 (95% CI = .70-.83) for ICU patients and .71 (95% CI = .64-.78) for surgical patients, and the CFS was .77 (95% CI = .70-.84) for ICU patients and .72 (95% CI = .65-.79) for surgical patients. The FI had good discriminative ability for prediction of 30-day mortality in ICU patients (multivariate odds ratio for each increase in FI of .1 = 2.04 [95% CI = 1.19-3.48]), comparable with the performance of the Acute Physiology and Chronic Health Evaluation III score (ICU patients) and the Portsmouth Physiological and Operative Severity Score for the Enumeration of Mortality and Morbidity score (surgical patients). CONCLUSION: It is feasible to construct an FI from hospital admission data in a cohort of critically ill and surgical patients.