N/A Would not let me proceed without entering an abstract despite this paper being a Letter to the Editor.

BACKGROUND:Frailty, malnutrition and low socioeconomic status may mutually perpetuate each other in a self-reinforcing and interdependent manner. The intertwined nature of these factors may be overlooked when investigating impacts on perioperative outcomes. This study aimed to investigate the impact of frailty, malnutrition and socioeconomic status on perioperative outcomes. METHODS:A multicentre cohort study involving six Australian tertiary hospitals was undertaken. All consecutive surgical patients who underwent an operation were included. Frailty was defined by the Hospital Frailty Risk Score, malnutrition by the Malnutrition Universal Screening Tool (MUST) and low socioeconomic status by the Index of Relative Socioeconomic Disadvantage. Linear mixed-effects and binary logistic generalised estimated equation models were performed for the outcomes: inpatient mortality, length of stay, 30-day readmission and re-operation. RESULTS:A total of 21 976 patients were included. After controlling for confounders, malnutrition and socioeconomic status, patients at high risk of frailty have a mean hospital length of stay 3.46 times longer (mean ratio = 3.46; 95% confidence interval (CI): 3.20, 3.73; P value < .001), odds of 30-day readmission 2.4 times higher (odds ratio = 2.40; 95% CI: 2.19, 2.63; P value < .001) and odds of in-hospital mortality 12.89 times greater than patients with low risk of frailty (odds ratio = 12.89; 95% CI: 4.51, 36.69; P value < .001). Elevated MUST scores were also significantly associated with worse outcomes, but to a lesser extent. Socioeconomic status had no association with outcomes. CONCLUSION:Perioperative risk evaluation should consider both frailty and malnutrition as separate, significant risk factors. Despite strong causal links with frailty and malnutrition, socioeconomic disadvantage is not associated with worse postoperative outcomes. Additional studies regarding the prospective identification of these patients with implementation of strategies to mitigate frailty and malnutrition and assessment of perioperative risk are required.

OBJECTIVE:To validate the International Study Group for Pancreatic Surgery (ISGPS) definition and grading system of post-pancreatectomy acute pancreatitis (PPAP) after pancreatoduodenectomy (PD). BACKGROUND:In 2022, the ISGPS defined PPAP and recommended a prospective validation of its diagnostic criteria and grading system. METHODS:This was a prospective, international, multicenter study including patients undergoing PD at 17 referral pancreatic centers across Europe, Asia, Oceania, and the United States. PPAP diagnosis required the following 3 parameters: (1) postoperative serum hyperamylasemia /hyperlipasemia (POH) persisting on postoperative days 1 and 2, (2) radiologic alterations consistent with PPAP, and (3) a clinically relevant deterioration in the patient's condition. To validate the grading system, clinical and economic parameters were analyzed across all grades. RESULTS:Among 2902 patients undergoing PD, 7.5% (n=218) developed PPAP (6.3% grade B and 1.2% grade C). POH occurred in 24.1% of patients. Hospital stay was associated with PPAP grades [no POH/PPAP 10 days [interquartile range (IQR): 7-17] days, grade B 22 days (IQR: 15-34) days, and grade C 43 days (IQR: 27-54) days; P <0.001], as well as intensive care unit admission (no POH/PPAP 5.4%, grade B 12.6%, grade C 82.9%; P <0.010), and hospital readmission rates (no POH/PPAP 7.3%, grade B 16.1%, grade C 18.5%; P <0.05). Costs of grade B and C PPAP were 2 and 11 times greater than uncomplicated clinical courses, respectively ( P <0.001). CONCLUSIONS:This first prospective, international validation study of the ISGPS definition and grading system for PPAP highlighted the relevant clinical and financial implications of this condition. These results stress the importance of routine screening for PPAP in patients undergoing PD.

Introduction: Reference ranges for determining pathological versus normal postoperative return of bowel function are not well characterised for general surgery patients. This study aimed to characterise time to first postoperative passage of stool after general surgery; determine associations between clinical factors and delayed time to first postoperative stool; and evaluate the association between delay to first postoperative stool and prolonged length of hospital stay. Methods: This study included consecutive admissions at two tertiary hospitals across a two-year period whom underwent a range of general surgery operations. Multivariable logistic regression analyses were conducted to determine associations between the explanatory variables and delayed first postoperative stool, and between delayed first postoperative stool and length of hospital stay. The previously specified explanatory variables were used, with the addition of the dichotomised >= 4-day delay to first postoperative stool. Prolonged length of hospital stay was considered >= 7 days. Results: 2,212 general surgery patients were included. Median time to first postoperative stool was 2.28 (IQR 1.06-3.96). Median length of stay was 7.19 (IQR 4.50-12.01). Several operative characteristics and medication exposures were associated with delayed first postoperative stool. There was a statistically significant association between delayed first postoperative stool (>= 4 days) and prolonged length of stay (>= 7 days) (OR 4.34, 95 %CI 3.27 to 5.77, p < 0.001). Conclusions: This study characterised expected reference ranges for time to return of bowel function across various general surgery operations and determined associations with clinical factors that may improve efficiency and identification of pathology within the postoperative course.

BACKGROUND:Although modern Australian healthcare systems provide patient-centred care, the ability to predict and prevent suboptimal post-procedural outcomes based on patient demographics at admission may improve health equity. This study aimed to identify patient demographic characteristics that might predict disparities in mortality, readmission, and discharge outcomes after either an operative or non-operative procedural hospital admission. METHODS:This retrospective cohort study included all surgical and non-surgical procedural admissions at three of the four major metropolitan public hospitals in South Australia in 2022. Multivariable logistic regression, with backwards selection, evaluated association between patient demographic characteristics and outcomes up to 90 days post-procedurally. RESULTS:40 882 admissions were included. Increased likelihood of all-cause, post-procedure mortality in-hospital, at 30 days, and 90 days, were significantly associated with increased age (P < 0.001), increased comorbidity burden (P < 0.001), an emergency admission (P < 0.001), and male sex (P = 0.046, P = 0.03, P < 0.001, respectively). Identification as ATSI (P < 0.001) and being born in Australia (P = 0.03, P = 0.001, respectively) were associated with an increased likelihood of 30-day hospital readmission and decreased likelihood of discharge directly home, as was increased comorbidity burden (P < 0.001) and emergency admission (P < 0.001). Being married (P < 0.001) and male sex (P = 0.003) were predictive of an increased likelihood of discharging directly home; in contrast to increased age (P < 0.001) which was predictive of decreased likelihood of this occurring. CONCLUSIONS:This study characterized several associations between patient demographic factors present on admission and outcomes after surgical and non-surgical procedures, that can be integrated within patient flow pathways through the Australian healthcare system to improve healthcare equity.

We read with interest the study by Jensen et al that demonstrated the feasibility of remote surgical triage at freestanding emergency departments (EDs).1 To complement this, we analyzed a prospectively maintained local database to understand the length of hospital stay (LOS) in general surgery patients who require inpatient admissions.

Internal Medicine JournalVolume 53, Issue 9 p. 1724-1725 Letter to the Editor Like a Surgeon? A letter commenting on Grosse and Thomas's 'Selection into training will always be an inexact process: a survey of Directors of Physician Education on selection into Basic Physician Training in Australia and New Zealand' Brandon Stretton, Brandon Stretton [email protected] orcid.org/0000-0002-7939-3489 Faculty of Health and Medical Sciences, University of Adelaide, Adelaide, South Australia, AustraliaSearch for more papers by this authorJoshua Kovoor, Joshua Kovoor orcid.org/0000-0002-3880-3840 Faculty of Health and Medical Sciences, University of Adelaide, Adelaide, South Australia, AustraliaSearch for more papers by this authorStephen Bacchi, Stephen Bacchi Faculty of Health and Medical Sciences, University of Adelaide, Adelaide, South Australia, AustraliaSearch for more papers by this authorAashray Gupta, Aashray Gupta Department of Cardiothoracic Surgery, Gold Coast University Hospital, Gold Coast, Queensland, AustraliaSearch for more papers by this authorThomas Hugh, Thomas Hugh Surgical Education Research and Training, Royal North Shore Hospital, Sydney, New South Wales, AustraliaSearch for more papers by this authorChristopher Dobbins, Christopher Dobbins Department of Surgery, Queen Elizabeth Hospital, Adelaide, South Australia, AustraliaSearch for more papers by this authorMarkus Trochsler, Markus Trochsler Department of Surgery, Queen Elizabeth Hospital, Adelaide, South Australia, AustraliaSearch for more papers by this authorPeter Hewett, Peter Hewett Department of Surgery, Queen Elizabeth Hospital, Adelaide, South Australia, AustraliaSearch for more papers by this authorWeng O. Chan, Weng O. Chan Department of Ophthalmology, Royal Adelaide Hospital, Adelaide, South Australia, AustraliaSearch for more papers by this authorSavio G. Barreto, Savio G. Barreto College of Medicine and Public Health, Flinders University, Adelaide, South Australia, Australia Hepatobiliary and Liver Transplant Unit, Flinders Medical Centre, Adelaide, South Australia, AustraliaSearch for more papers by this authorChristopher Rayner, Christopher Rayner Faculty of Health and Medical Sciences, University of Adelaide, Adelaide, South Australia, AustraliaSearch for more papers by this authorMartin Bruening, Martin Bruening Department of Cardiothoracic Surgery, Gold Coast University Hospital, Gold Coast, Queensland, AustraliaSearch for more papers by this authorRobert Padbury, Robert Padbury College of Medicine and Public Health, Flinders University, Adelaide, South Australia, AustraliaSearch for more papers by this authorNicholas J. Talley, Nicholas J. Talley School of Medicine and Public Health, University of Newcastle, Newcastle, New South Wales, AustraliaSearch for more papers by this authorAdrian Anthony, Adrian Anthony Royal Australasian College of Surgeons, Melbourne, Victoria, AustraliaSearch for more papers by this authorMichael Horowitz, Michael Horowitz Faculty of Health and Medical Sciences, University of Adelaide, Adelaide, South Australia, AustraliaSearch for more papers by this authorGuy Maddern, Guy Maddern Department of Cardiothoracic Surgery, Gold Coast University Hospital, Gold Coast, Queensland, AustraliaSearch for more papers by this authorMark Boyd, Mark Boyd Faculty of Health and Medical Sciences, University of Adelaide, Adelaide, South Australia, AustraliaSearch for more papers by this author Brandon Stretton, Brandon Stretton [email protected] orcid.org/0000-0002-7939-3489 Faculty of Health and Medical Sciences, University of Adelaide, Adelaide, South Australia, AustraliaSearch for more papers by this authorJoshua Kovoor, Joshua Kovoor orcid.org/0000-0002-3880-3840 Faculty of Health and Medical Sciences, University of Adelaide, Adelaide, South Australia, AustraliaSearch for more papers by this authorStephen Bacchi, Stephen Bacchi Faculty of Health and Medical Sciences, University of Adelaide, Adelaide, South Australia, AustraliaSearch for more papers by this authorAashray Gupta, Aashray Gupta Department of Cardiothoracic Surgery, Gold Coast University Hospital, Gold Coast, Queensland, AustraliaSearch for more papers by this authorThomas Hugh, Thomas Hugh Surgical Education Research and Training, Royal North Shore Hospital, Sydney, New South Wales, AustraliaSearch for more papers by this authorChristopher Dobbins, Christopher Dobbins Department of Surgery, Queen Elizabeth Hospital, Adelaide, South Australia, AustraliaSearch for more papers by this authorMarkus Trochsler, Markus Trochsler Department of Surgery, Queen Elizabeth Hospital, Adelaide, South Australia, AustraliaSearch for more papers by this authorPeter Hewett, Peter Hewett Department of Surgery, Queen Elizabeth Hospital, Adelaide, South Australia, AustraliaSearch for more papers by this authorWeng O. Chan, Weng O. Chan Department of Ophthalmology, Royal Adelaide Hospital, Adelaide, South Australia, AustraliaSearch for more papers by this authorSavio G. Barreto, Savio G. Barreto College of Medicine and Public Health, Flinders University, Adelaide, South Australia, Australia Hepatobiliary and Liver Transplant Unit, Flinders Medical Centre, Adelaide, South Australia, AustraliaSearch for more papers by this authorChristopher Rayner, Christopher Rayner Faculty of Health and Medical Sciences, University of Adelaide, Adelaide, South Australia, AustraliaSearch for more papers by this authorMartin Bruening, Martin Bruening Department of Cardiothoracic Surgery, Gold Coast University Hospital, Gold Coast, Queensland, AustraliaSearch for more papers by this authorRobert Padbury, Robert Padbury College of Medicine and Public Health, Flinders University, Adelaide, South Australia, AustraliaSearch for more papers by this authorNicholas J. Talley, Nicholas J. Talley School of Medicine and Public Health, University of Newcastle, Newcastle, New South Wales, AustraliaSearch for more papers by this authorAdrian Anthony, Adrian Anthony Royal Australasian College of Surgeons, Melbourne, Victoria, AustraliaSearch for more papers by this authorMichael Horowitz, Michael Horowitz Faculty of Health and Medical Sciences, University of Adelaide, Adelaide, South Australia, AustraliaSearch for more papers by this authorGuy Maddern, Guy Maddern Department of Cardiothoracic Surgery, Gold Coast University Hospital, Gold Coast, Queensland, AustraliaSearch for more papers by this authorMark Boyd, Mark Boyd Faculty of Health and Medical Sciences, University of Adelaide, Adelaide, South Australia, AustraliaSearch for more papers by this author First published: 24 September 2023 https://doi.org/10.1111/imj.16214Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onEmailFacebookTwitterLinkedInRedditWechat No abstract is available for this article. References 1Grosse A, Thomas J. 'Selection into training will always be an inexact process': a survey of directors of physician education on selection into basic physician training in Australia and New Zealand. Intern Med J 2023. https://doi.org/10.1111/imj.16083 10.1111/imj.16083 PubMedWeb of Science®Google Scholar 2Brennan P. Trainee Selection in Australian Medical Colleges. Medical Training Review Panel Commonwealth Department of Health and Family Services, Pub; 1998. Google Scholar 3 RACS Surgical Competence and Performance Framework – A Guide to Aid the Assessment and Development of Surgeons. 3rd ed, 2020. Australia: RACS. Available from URL: https://www.surgeons.org/-/media/Project/RACS/surgeons-org/files/Louise-Pfrunder/Surgical-Competence-and-Performance-Framework_V16.pdf?rev=120ec964eb9e4c26a1f266eba17eaf79&hash=BA49820B4620667CA9F007C72856E346 Google Scholar 4Oldfield Z, Beasley S, Smith J, Anthony A, Watt A. Correlation of selection scores with assessment scores during surgical training. ANZ J Surg 2013; 83: 412–416. 10.1111/ans.12176 CASPubMedWeb of Science®Google Scholar Volume53, Issue9September 2023Pages 1724-1725 ReferencesRelatedInformation

Readmission is a poor outcome for both patients and healthcare systems. The association of certain sociocultural and demographic characteristics with likelihood of readmission is uncertain in general surgical patients. A multi-centre retrospective cohort study of consecutive unique individuals who survived to discharge during general surgical admissions was conducted. Sociocultural and demographic variables were evaluated alongside clinical parameters (considered both as raw values and their proportion of change in the 1–2 days prior to admission) for their association with 7 and 30 days readmission using logistic regression. There were 12,701 individuals included, with 304 (2.4

Purpose: The Health and Disability Commissioner (HDC) is responsible for receiving, investigating, and making decisions regarding medical complaints. This paper aims to review the investigated cases from all surgical specialties that have undergone formal review by the HDC, speci fi cally assessing trends, type of breach and recommendations. Methodology: All case decisions and annual-reports from the HDC website from year ending June 1997 to 2021 were reviewed with surgical cases selected and details extracted. Results: Of the 1559 total decisions available for review, 144 (9%) involved surgical specialties. 90% of total surgical cases that underwent a formal review resulted in a breach in the code of rights.

The applicability of the vital signs prompting medical emergency response (MER) activation has not previously been examined specifically in a large general surgical cohort. This study aimed to characterize the distribution, and predictive performance, of four vital signs selected based on Australian guidelines (oxygen saturation, respiratory rate, systolic blood pressure and heart rate); with those of the MER activation criteria.

BACKGROUND:Colorectal cancer with synchronous liver-only metastasis is managed with a multimodal approach, however, optimal sequencing of modalities remains unclear.METHODS:A retrospective review of all consecutive rectal or colon cancer cases with synchronous liver-only metastasis was conducted from the South Australian Colorectal Cancer Registry from 2006 to 2021. This study aimed to investigate how order and type of treatment modality affects overall survival.RESULTS:Data of over 5000 cases were analysed (n = 5244), 1420 cases had liver-only metastasis. There were a greater number of colon than rectal primaries (N = 1056 versus 364). Colonic resection was the preferred initial treatment for the colon cohort (60%). In the rectal cohort, 30% had upfront resection followed by 27% that had chemo-radiotherapy as 1st line therapy. For the colon cohort, there was an improved 5-year survival with surgical resection as initial treatment compared to chemotherapy (25% versus 9%, P < 0.001). In the rectal cohort, chemo-radiotherapy as the initial treatment was associated with an improved 5-year survival compared to surgery or chemotherapy (40% versus 26% versus 19%, P = 0.0015). Patients who were able to have liver resection had improved survival, with 50% surviving over 5 years compared to 12 months in the non-resected group (P < 0.001). Primary rectal KRAS wildtype patients who underwent liver resection and received Cetuximab had significantly worse outcomes compared to KRAS wildtype patients who did not (P = 0.0007).CONCLUSIONS:Where surgery is possible, resection of liver metastasis and primary tumour improved overall survival. Further research is required on the use of targeted treatments in patients undergoing liver resection.

ANZ Journal of SurgeryVolume 93, Issue 7-8 p. 1756-1757 PERSPECTIVE ‘Hurry up’ syndrome harms surgical patients and staff Joshua G. Kovoor MBBS, Joshua G. Kovoor MBBS orcid.org/0000-0002-3880-3840 Flinders Medical Centre, Adelaide, South Australia, Australia Queen Elizabeth Hospital, Adelaide, South Australia, Australia University of Adelaide, Adelaide, South Australia, Australia Royal Adelaide Hospital, Adelaide, South Australia, Australia Health and Information, Adelaide, South Australia, AustraliaSearch for more papers by this authorAashray K. Gupta MS, Aashray K. Gupta MS orcid.org/0000-0002-8038-0378 University of Adelaide, Adelaide, South Australia, Australia Health and Information, Adelaide, South Australia, Australia Gold Coast University Hospital, Gold Coast, Queensland, AustraliaSearch for more papers by this authorStephen Bacchi PhD, Stephen Bacchi PhD Queen Elizabeth Hospital, Adelaide, South Australia, Australia University of Adelaide, Adelaide, South Australia, Australia Royal Adelaide Hospital, Adelaide, South Australia, Australia Health and Information, Adelaide, South Australia, AustraliaSearch for more papers by this authorBrandon Stretton MBBS, Brandon Stretton MBBS orcid.org/0000-0002-7939-3489 Queen Elizabeth Hospital, Adelaide, South Australia, Australia University of Adelaide, Adelaide, South Australia, Australia Royal Adelaide Hospital, Adelaide, South Australia, Australia Health and Information, Adelaide, South Australia, AustraliaSearch for more papers by this authorRobert T. Padbury PhD, Robert T. Padbury PhD Flinders Medical Centre, Adelaide, South Australia, AustraliaSearch for more papers by this author Joshua G. Kovoor MBBS, Joshua G. Kovoor MBBS orcid.org/0000-0002-3880-3840 Flinders Medical Centre, Adelaide, South Australia, Australia Queen Elizabeth Hospital, Adelaide, South Australia, Australia University of Adelaide, Adelaide, South Australia, Australia Royal Adelaide Hospital, Adelaide, South Australia, Australia Health and Information, Adelaide, South Australia, AustraliaSearch for more papers by this authorAashray K. Gupta MS, Aashray K. Gupta MS orcid.org/0000-0002-8038-0378 University of Adelaide, Adelaide, South Australia, Australia Health and Information, Adelaide, South Australia, Australia Gold Coast University Hospital, Gold Coast, Queensland, AustraliaSearch for more papers by this authorStephen Bacchi PhD, Stephen Bacchi PhD Queen Elizabeth Hospital, Adelaide, South Australia, Australia University of Adelaide, Adelaide, South Australia, Australia Royal Adelaide Hospital, Adelaide, South Australia, Australia Health and Information, Adelaide, South Australia, AustraliaSearch for more papers by this authorBrandon Stretton MBBS, Brandon Stretton MBBS orcid.org/0000-0002-7939-3489 Queen Elizabeth Hospital, Adelaide, South Australia, Australia University of Adelaide, Adelaide, South Australia, Australia Royal Adelaide Hospital, Adelaide, South Australia, Australia Health and Information, Adelaide, South Australia, AustraliaSearch for more papers by this authorRobert T. Padbury PhD, Robert T. Padbury PhD Flinders Medical Centre, Adelaide, South Australia, AustraliaSearch for more papers by this author First published: 17 July 2023 https://doi.org/10.1111/ans.18593Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onEmailFacebookTwitterLinkedInRedditWechat No abstract is available for this article. References 1McElhatton J, Drew C. Hurry-Up Syndrome. Edition. [Cited 2 April 2023.] Available from URL: https://asrs.arc.nasa.gov/publications/directline/dl5_hurry.htm 2McElhatton J, Drew C. “Hurry up” Syndrome. Air Line Pilot; 1994. 3McElhatton J, Drew C. ‘Hurry-Up’ Syndrome Identified as a Causal Factor In Aviation Safety Incidents. Edition[Cited 2 April 2023.] Available from: https://flightsafety.org/hf/hf_sep-oct93.pdf 4Kanki BG, Anca J, Chidester TR. Crew Resource Management. London, UK: Academic Press; 2019. 5Sullivan C, Staib A, Khanna S et al. The National Emergency Access Target (NEAT) and the 4-hour rule: time to review the target. Med. J. Aust. 2016; 204: 354–4. 6Crawford SM. Goodhart's law: when waiting times became a target, they stopped being a good measure. BMJ 2017; 359: j5425. 7Healey A, Undre S, Vincent C. Developing observational measures of performance in surgical teams. BMJ Qual. Safety 2004; 13: i33–40. 8Collaborative S, writing group for the SCOAP T, Kwon S et al. Creating a learning healthcare system in surgery: Washington State's surgical care and outcomes assessment program (SCOAP) at 5 years. Surgery 2012; 151: 146–52. 9Reason J. Human error: models and management. BMJ 2000; 320: 768–70. 10Meara JG, Leather AJ, Hagander L et al. Global surgery 2030: evidence and solutions for achieving health, welfare, and economic development. Lancet 2015; 386: 569–624. 11Tsiga E, Panagopoulou E, Sevdalis N, Montgomery A, Benos A. The influence of time pressure on adherence to guidelines in primary care: an experimental study. BMJ Open 2013; 3: e002700. 12Arora S, Sevdalis N, Nestel D, Woloshynowych M, Darzi A, Kneebone R. The impact of stress on surgical performance: a systematic review of the literature. Surgery 2010; 147: e316. 13Blendon RJ, DesRoches CM, Brodie M et al. Views of practicing physicians and the public on medical errors. N. Engl. J. Med. 2002; 347: 1933–40. 14Pham JC, Aswani MS, Rosen M et al. Reducing medical errors and adverse events. Annu. Rev. Med. 2012; 63: 447–63. 15Cooper WO, Guillamondegui O, Hines OJ et al. Use of unsolicited patient observations to identify surgeons with increased risk for postoperative complications. JAMA Surg. 2017; 152: 522–9. 16Cooper WO, Spain DA, Guillamondegui O et al. Association of coworker reports about unprofessional behavior by surgeons with surgical complications in their patients. JAMA Surg. 2019; 154: 828–34. 17Andel C, Davidow SL, Hollander M, Moreno DA. The economics of health care quality and medical errors. J. Health Care Finance 2012; 39: 39–50. 18Gallagher TH, Waterman AD, Ebers AG, Fraser VJ, Levinson W. Patients' and physicians' attitudes regarding the disclosure of medical errors. JAMA 2003; 289: 1001–7. 19Shanafelt TD, Balch CM, Bechamps G et al. Burnout and medical errors among American surgeons. Ann. Surg. 2010; 251: 995–1000. Volume93, Issue7-8July/August 2023Pages 1756-1757 ReferencesRelatedInformation

Background Depression is the leading cause of global disability and can develop following the change in body image and functional capacity associated with stoma surgery. However, reported prevalence across the literature is unknown. Accordingly, we performed a systematic review and meta-analysis aiming to characterise depressive symptoms after stoma surgery and potential predictive factors. Methods PubMed/MEDLINE, Embase, CINAHL and Cochrane Library were searched from respective database inception to 6 March 2023 for studies reporting rates of depressive symptoms after stoma surgery. Risk of bias was assessed using the Downs and Black checklist for non-randomised studies of interventions (NRSIs), and Cochrane RoB2 tool for randomised controlled trials (RCTs). Meta-analysis incorporated meta-regressions and a random-effects model. Registration: PROSPERO, CRD42021262345. Results From 5,742 records, 68 studies were included. According to Downs and Black checklist, the 65 NRSIs were of low to moderate methodological quality. According to Cochrane RoB2, the three RCTs ranged from low risk of bias to some concerns of bias. Thirty-eight studies reported rates of depressive symptoms after stoma surgery as a proportion of the respective study populations, and from these, the median rate across all timepoints was 42.9% 42.9% (IQR: 24.2–58.9%). Pooled scores for respective validated depression measures (Hospital Anxiety and Depression Score (HADS), Beck Depression Inventory (BDI), and Patient Health Questionnaire-9 (PHQ-9)) across studies reporting those scores were below clinical thresholds for major depressive disorder according to severity criteria of the respective scores. In the three studies that used the HADS to compare non-stoma versus stoma surgical populations, depressive symptoms were 58% less frequent in non-stoma populations. Region (Asia–Pacific; Europe; Middle East/Africa; North America) was significantly associated with postoperative depressive symptoms ( p = 0.002), whereas age ( p = 0.592) and sex ( p = 0.069) were not. Conclusions Depressive symptoms occur in almost half of stoma surgery patients, which is higher than the general population, and many inflammatory bowel disease and colorectal cancer populations outlined in the literature. However, validated measures suggest this is mostly at a level of clinical severity below major depressive disorder. Stoma patient outcomes and postoperative psychosocial adjustment may be enhanced by increased psychological evaluation and care in the perioperative period.

BACKGROUND:This study aimed to examine the accuracy with which multiple natural language processing artificial intelligence models could predict discharge and readmissions after general surgery.METHODS:Natural language processing models were derived and validated to predict discharge within the next 48 hours and 7 days and readmission within 30 days (based on daily ward round notes and discharge summaries, respectively) for general surgery inpatients at 2 South Australian hospitals. Natural language processing models included logistic regression, artificial neural networks, and Bidirectional Encoder Representations from Transformers.RESULTS:For discharge prediction analyses, 14,690 admissions were included. For readmission prediction analyses, 12,457 patients were included. For prediction of discharge within 48 hours, derivation and validation data set area under the receiver operator characteristic curves were, respectively: 0.86 and 0.86 for Bidirectional Encoder Representations from Transformers, 0.82 and 0.81 for logistic regression, and 0.82 and 0.81 for artificial neural networks. For prediction of discharge within 7 days, derivation and validation data set area under the receiver operator characteristic curves were, respectively: 0.82 and 0.81 for Bidirectional Encoder Representations from Transformers, 0.75 and 0.72 for logistic regression, and 0.68 and 0.67 for artificial neural networks. For readmission prediction within 30 days, derivation and validation data set area under the receiver operator characteristic curves were, respectively: 0.55 and 0.59 for Bidirectional Encoder Representations from Transformers and 0.77 and 0.62 for logistic regression.CONCLUSION:Modern natural language processing models, particularly Bidirectional Encoder Representations from Transformers, can effectively and accurately identify general surgery patients who will be discharged in the next 48 hours. However, these approaches are less capable of identifying general surgery patients who will be discharged within the next 7 days or who will experience readmission within 30 days of discharge.

As clinicians and researchers, it is our responsibility to use the data available to us to optimise the care of our patients. These data are vast and should, in theory, be able to provide granular information to make appropriate diagnostic and management decisions to provide the best care for patients. However, these data can take many forms in electronic medical records (EMR) that are also not designed to output the information in a coordinated manner. Accordingly, the measurement of data in EMR is often tenuous and slow. Now, with many institutions transitioning from handwritten to EMR software, foresight and a coordinated approach is required to transition to the digital age. Furthermore, EMR data has the potential to facilitate future audit and research activities through permitting the expediate collection of epidemiological data to predict outcomes and possibly select treatments.1 There is, however, a disconnect in the EMR design process, between software engineers and clinicians, which is undermining quality improvement initiatives. The design, implementation and operationalisation of functional systems that collects, exchanges and uses patient data to promote health care practice evolution and improved quality care has not been systematic and is poorly transparent.2 Medical text data are recorded electronically for multiple reasons, including as a means of communication, as a legal record, and for subsequent unit audit and research activities. Hospital audit activities are vitally important, and are necessary to maintain and improve the quality of hospital services.3 Research activities facilitate the development of novel strategies and treatments to improve care. Both of these activities are vital to hospital functioning and growth, and both require data. The type of data that can facilitate these activities is ideally standardized, clean and comprehensive. ICD10 codes are often used as a proxy by administrators for such data and whilst they are the best available option, they have significant flaws in their accuracy, particularly with respect to hospital acquired complications and are not granular enough to be used as a quality comparator.4 The stereotypical clinical workflow involves patient interaction, data input, integration and output/ extraction (both correspondence and audit quality indicators/performance metrics) and can either be challenged or optimised with EMR integration. A concerted approach to EMR operationalisation can optimise all aspects of this workflow by; modulating data input, and artificial intelligence (AI) incorporation to streamline data integration and output. Electronic medical record modification is a strategy that can be employed to optimise data input and increase the utility of routinely collected medical data. Through the use of online forms, mandatory fields and carefully designed proformas, it is theoretically possible to standardize the recording of medical text data. However, these strategies may not succeed in practice due to the workflows of staff members that are required to use these methods of documentation. For example, when online forms are developed in a manner that is cumbersome, staff may frequently develop means to complete their work in an efficient manner that circumvent the desired effect of the document structure. For example, when a given field is mandatory, a Hospital Medical Officer(HMO) under time pressure may put a single character in the field, so that it is marked as complete, while completing the majority of documentation in a single field of a given document. Additionally, making EMR modifications may be a slow and time-consuming process. Routinely collected data may also be optimised by incorporation of recordings/dictations for telehealth consults and multi-disciplinary meetings and ensuring compatible data sharing with all services from primary to quaternary level care. The use of templates is a comparatively simple means of improving data input and the value of routine medical data collection. By providing structured text for certain types of documentation (such as in the form of an 'acronym expansion' or a distributed document), healthcare workers are not only provided with a means by which their routine data collection may be standardized, but a recommendation regarding the components of an assessment that should take place. Offering a healthcare professional this template may also serve as a 'checklist' for given documents. For example, when a HMO is provided with a template for discharge summaries, this provides the HMO with guidance as to the type and level of information required in such discharge summaries. 'Checklist' approaches have previously been successful in other areas in surgery.5 Other advantages of template strategies include their ease of usability (as opposed to EMR modification) and agility (able to be modified at a departmental or institutional level quickly in response to changing clinical environments). In addition to discharge summaries, other examples of times when a template-based strategy may be useful include preadmission notes (see example Supplementary Information 1), ward round notes, and multi-disciplinary meeting notes. AI technologies including natural language processing (NLP) and generative AI are additional strategies that may be employed to improve data output, particularly when data input is unable to be optimised. NLP involves the application of computers to human language – either as speech or as text. Previous studies have identified that NLP can obtain meaningful unit audit activity data from medical free-text, such as discharge summaries.6 Furthermore, NLP has also been found to reduce the amount of missing crucial data (such as vital signs) within unstructured notes in electronic medical records.7 However, there are limitations to the level of performance that this type of method can obtain and, ultimately, the approach is limited to the information that is recorded, and what the users enter.8 In other words, if the data are not recorded, this method cannot obtain the information. Large language models (LLM) are another type of AI that has been successfully integrated into the clinical workflow to optimise output, specifically, producing patient clinic letters with high overall correctness, humanness and at a reading level that is broadly similar to current real-world human-generated letters.9 These strategies, either used in isolation or in combination, require a supportive local environment and vested stakeholders to be successful. Senior clinical staff have a critical role to play in the effective use of these strategies, ensuring hospital preparedness by liaising with appropriate senior administerial and information-technology representatives. The ongoing encouragement of junior staff to utilize the endorsed methods of data recording is also required. If the system is to work, there needs to be quality assurance (via clinically appropriate and regular audits) with senior clinician input, and not just set and forget at the start. Similarly, junior staff have a responsibility to support each other and should be incentivized to maintain the level of documentation that is required and engage in the audit process. Ultimately, an understanding that the standardized recording of information is an imperative role in the maintenance and optimisation of care should be fundamental to these initiatives. Medical text data are recorded every day in large quantities at institutions throughout the world. The transition from handwritten to EMR is inevitable, and clinicians need to embrace this process of evolution and engage in its implementation and improvement. Otherwise, it will be deferred to non-medical personnel at the potential cost of advancing patient care. Clinician engagement in EMR optimisation, using their inherent medical expertise and guided by evidence, can advocate for systems improvements in critical areas that might otherwise be forlorn by those without a clinical background including; transferability of data between medical systems (hospital networks and community practices), telehealth/conferencing integrations, clinical decision support system incorporation and adequate training modules for prevocational students.10, 11 Regarding this transition, local systems will dictate whether the current incremental change occurring currently is sufficient or if there should be an overhaul. This transition however must occur in parallel with appropriate cybersecurity implementation. Health institutions have a medico-legal and ethical responsibility to ensure the medical information adheres to good information security management practices to ensure confidentiality and integrity is assured. The information must also be processed and protected at a level commensurate with the classification to mitigate internal and external threats such as data leaks. Access to information must be regulated in accordance with legislative and business requirements, classifications and accessed only on a 'need to know', 'least privilege' principle and justifiable business means. Cryptographic controls are one example of how confidentiality and data may be protected, where cryptographic algorithm, protocols and key lengths are configured in accordance with Federal Government's Information Security Manual. Through the use of strategies including NLP, EMR optimisation, and templates, the utility of this routinely recorded medical data could be increased substantially. These data, obtained through routine activity, may provide novel insights to improve the outcomes of our patients. Open access publishing facilitated by The University of Adelaide, as part of the Wiley - The University of Adelaide agreement via the Council of Australian University Librarians. Brandon Stretton: Conceptualization; methodology; visualization; writing – original draft; writing – review and editing. Robert Padbury: Supervision; validation; visualization; writing – review and editing. Markus Trochsler: Supervision; validation; writing – review and editing. Thomas J. Hugh: Conceptualization; project administration; supervision; validation; visualization; writing – review and editing. Guy Maddern: Conceptualization; project administration; supervision; validation; visualization; writing – review and editing. Mark Boyd: Conceptualization; project administration; resources; supervision; validation; visualization; writing – review and editing. Lewis Hains: Visualization; writing – review and editing. Aashray Gupta: Methodology; supervision; visualization; writing – original draft; writing – review and editing. Joshua Kovoor: Conceptualization; investigation; visualization; writing – original draft; writing – review and editing. Stephen Bacchi: Conceptualization; supervision; validation; visualization; writing – original draft; writing – review and editing. Savio Barreto: Supervision; validation; visualization; writing – review and editing. Patrick G. O'Callaghan: Supervision; validation; visualization; writing – review and editing. Bianca Wong: Conceptualization; supervision; validation; visualization; writing – review and editing. Elizabeth Murphy: Supervision; validation; writing – review and editing. This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors. Supplementary Information 1 Example preadmissions template (Figure). Please note: The publisher is not responsible for the content or functionality of any supporting information supplied by the authors. Any queries (other than missing content) should be directed to the corresponding author for the article.

COVID-19 has changed surgery worldwide. As surgical outcomes for patients with COVID-19 are significantly poorer than those without, one of the most important preventative measures for surgical safety has been vaccination, which dramatically reduces transmission and disease severity. Predicated largely on favourable phase II/III clinical trials, several vaccines have been rolled out internationally with phase IV outcomes meeting expectations. Despite evidence supporting the safety and efficacy, it has been challenging to develop evidence-based guidelines for safely providing vaccination and surgical care worldwide. Consideration of risks is necessary at the individual patient level. This perspective piece aimed to explore factors relating to available COVID-19 and provide recommendations for undertaking surgery in those who have been recently vaccinated. For each clinical statement made, a level of evidence, according to the evidence hierarchy outlined by Merlin et al., is provided. The levels of evidence provided represent the body of literature retrieved in a report undertaken by the Royal Australasian College of Surgeons that incorporated a formal search strategy. This article represents a major collaborative effort, and all listed authors contributed to the manuscript’s conception, analysis and interpretation of data, revised the article critically for important intellectual content, and provided final approval of the version to be published. Reactogenicity refers to the expected, transient reactions occurring after vaccination and is common after COVID-19 vaccinations. The typical influenza-like symptoms (e.g. pain, fatigue, headache, chills and myalgia), are generally mild and self-limiting, lasting one to 3 days with few events observed after seven. It disproportionally burdens adults under 65, females, those with past COVID-19, or obesity. Reactogenicity following COVID-19 vaccinations is important for perioperative management, as symptoms may prevent accurate assessment of surgical risk preoperatively, and may mimic symptoms of infection postoperatively. Staff should be vigilant if a patient has been recently vaccinated, and any symptoms investigated to ascertain whether they stem from expected reactogenicity or surgical pathology (level II evidence). Adverse events of special interest (AESI) are adverse events associated with COVID-19 vaccines or specific vaccine platforms. A range of AESI have been reported following vaccination, however for most it is unclear whether their incidence surpasses backgrounds rates, whether they have been clinically verified, or whether they are causally associated with COVID-19 vaccines. Presently, reported AESI with causal or suspected causal links to COVID-19 vaccines include Guillain-Barre syndrome (GBS), myocarditis and pericarditis, and thrombosis with thrombocytopenia syndrome (TTS). These generally occur within 2 weeks post vaccination. TTS has occurred more frequently after first doses of the Oxford-AstraZeneca vaccine, whereas myocarditis and pericarditis have occurred more frequently following second doses of the Moderna and Pfizer-BioNTech. Younger adults appear to be disproportionally affected, but risk factors remain to be fully elucidated. Current evidence suggests these events may be serious, but are extremely rare. If a surgical patient experiences adverse events related to COVID-19 vaccination, operative delay until resolution should be considered given potential alteration of cardiovascular and clotting functions (level II evidence). Staff should familiarize themselves with current guidelines relating to TTS, GBS and myocarditis and pericarditis, so that required management can be optimized. Of note, thrombosis may be worsened in the presence of heparin due to enhanced platelet activation in TTS, hence heparins should be avoided and direct anticoagulants used instead (level II evidence). Data from large clinical trials suggest that immunity against SARS-CoV-2 is generally reached between 7 days (Pfizer-BioNTech) to 14 days (Oxford-AstraZeneca, Gamaleya, Janssen, Moderna, Novavax, Sinovac and Sinopharm) following final vaccine dose. Allowing at least 14 days after vaccination enables development of optimal immune responses, minimizing risk of nosocomial acquisition or transmission (level II evidence). Patients with immunological deficiencies or haematological malignancy may not develop protective immune responses following vaccination. Regardless, rates of COVID-19 are dramatically higher for unvaccinated versus vaccinated persons, and vaccination should always be advised. Surgery and anaesthesia may dysregulate the immune system, an effect potentially persisting for some weeks. This may in turn affect COVID-19 vaccine efficacy. Booster vaccine doses may also be necessary at a population level as data suggest that vaccine-induced immunity wanes. On average, vaccine efficacy or effectiveness against SARS-CoV-2 infection decreases from 1–6 months after full vaccination by over 20% across people of all ages. This should be evidence-based and prioritized according to patient risk. Crucial to safe clinical decision-making is patient stratification by operative urgency, level of morbidity and co-morbidities (level III-2 to IV evidence). Case-by-case evaluation should incorporate: age; comorbidities associated with COVID-19-related risk such as hypertension, diabetes, cardiovascular or pulmonary disease, and immunocompromise; severity of surgical pathology; individual wishes; and likelihood of active SARS-CoV-2 infection (level III-2 to IV evidence). As vaccination against COVID-19 significantly

Objective: The ISGPS aimed to develop a universally accepted definition for PPAP for standardized reporting and outcome comparison. Background: : PPAP is an increasingly recognized complication after partial pancreatic resections, but its incidence and clinical impact, and even its existence are variable because an internationally accepted consensus definition and grading system are lacking. Methods: The ISGPS developed a consensus definition and grading of PPAP with its members after an evidence review and after a series of discussions and multiple revisions from April 2020 to May 2021. Results: We defined PPAP as an acute inflammatory condition of the pancreatic remnant beginning within the first 3 postoperative days after a partial pancreatic resection. The diagnosis requires (1) a sustained postoperative serum hyperamylasemia (POH) greater than the institutional upper limit of normal for at least the first 48 hours postoperatively, (2) associated with clinically relevant features, and (3) radiologic alterations consistent with PPAP. Three different PPAP grades were defined based on the clinical impact: (1) grade postoperative hyperamylasemia, biochemical changes only; (2) grade B, mild or moderate complications; and (3) grade C, severe life-threatening complications. Discussions: The present definition and grading scale of PPAP, based on biochemical, radiologic, and clinical criteria, are instrumental for a better understanding of PPAP and the spectrum of postoperative complications related to this emerging entity. The current terminology will serve as a reference point for standard assessment and lend itself to developing specific treatments and prevention strategies.

Background Enhanced Recovery After Surgery (ERAS) has been widely applied in liver surgery since the publication of the first ERAS guidelines in 2016. The aim of the present article was to update the ERAS guidelines in liver surgery using a modified Delphi method based on a systematic review of the literature. Methods A systematic literature review was performed using MEDLINE/PubMed, Embase, and the Cochrane Library. A modified Delphi method including 15 international experts was used. Consensus was judged to be reached when >80% of the experts agreed on the recommended items. Recommendations were based on the Grading of Recommendations, Assessment, Development and Evaluations system. Results A total of 7541 manuscripts were screened, and 240 articles were finally included. Twenty-five recommendation items were elaborated. All of them obtained consensus (>80% agreement) after 3 Delphi rounds. Nine items (36%) had a high level of evidence and 16 (64%) a strong recommendation grade. Compared to the first ERAS guidelines published, 3 novel items were introduced: prehabilitation in high-risk patients, preoperative biliary drainage in cholestatic liver, and preoperative smoking and alcohol cessation at least 4 weeks before hepatectomy. Conclusions These guidelines based on the best available evidence allow standardization of the perioperative management of patients undergoing liver surgery. Specific studies on hepatectomy in cirrhotic patients following an ERAS program are still needed.

Treatment of mCRC is guided by clinical and molecular features which include side of primary, RAS, BRAF and MMR status. For left sided RAS WT mCRC survival is optimized by using first-line anti-EGFR anti-bodies combined with chemotherapy. We aim to assess the use of first-line anti-EGFR/chemotherapy (FaEC) combinations in patients with mCRC and assess for differences between cetuximab (C) and panitumumab (P) using the SA mCRCR. This real word registry has collected data from all patients diagnosed with mCRC in SA prospectively since 2/2006. We compared C and P in RAS WT patients, and those treated with bevacizumab (B) from 2006 and those treated since January 2015 when FaEC was funded in Australia. Survival was analysed using the Kaplan Meier method. Of the 5537 patients currently entered onto the registry, 1313 had RAS status recorded and 245 received FaEC (167/68% since 2015). 1068 patients received B (52% KRAS WT). Table summarises patient characteristics and median OS for FaEC (C or P) and B. Overall there was no statistical difference in survival for C v P (p=0.125). Patients entered from 2015 had mostly similar patient characteristics including significant use in right sided primary (24% v 20% respectively).Table: 403PChemo/C (139)Chemo/P (106)Chemo/B (1068)Median age (range)65.3 yrs (24-87)60.1 yrs (26-89)64.6 (20.5-93)Male66.2%64%61%Oxaliplatin15%43.4%70%Irinotecan59%46.2%15.5%Stage 4 at diagnosis47.5%64.2%67.5%Left primary65.5%77.4%58%Liver mets only38.1%42.5%37.2%Lung mets only5.8%9.4%8.7%BRAF MT7.9%2.8%7.3%Liver resection6.5%14.2%9.8%Median OS (95% CI)21.6 mths (16.7-26.4)25.3 mths (20.8-29.7)22.7 mths (21-23.9) Open table in a new tab When comparing C & P in first-line therapy, C was more often combined with irinotecan chemo. There were lower rates of liver resection and higher right primary and BRAF MT in patients treated with C which may explain the numerically lower median overall survival.