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Background: The frequency of oxycodone adverse reactions, subsequent opioid prescription, effect on pain and patient care in general surgery patients are not well known. This study aimed to determine prevalence of documented oxycodone allergy and intolerances (independent variables) in a general surgical cohort, and association with prescribing other analgesics (particularly opioids), subjective pain scores, and length of hospital stay (dependent variables). Methods: This retrospective cohort study included general surgery patients from two South Australian hospitals between April 2020 and March 2022. Multivariable logistic regression evaluated associations between previous oxycodone allergies and intolerances, prescription records, subjective pain scores, and length of hospital stay. Results: Of 12 846 patients, 216 (1.7%) had oxycodone allergies, and 84 (0.7%) oxycodone intolerances. The 216 oxycodone allergy patients had lower odds of receiving oxycodone (OR 0.17, P < 0.001), higher odds of tramadol (OR 3.01, P < 0.001) and tapentadol (OR 2.87, P = 0.001), but 91 (42.3%) still received oxycodone and 19 (8.8%) morphine. The 84 with oxycodone intolerance patients had lower odds of receiving oxycodone (OR 0.23, P < 0.001), higher odds of fentanyl (OR 3.6, P < 0.001) and tramadol (OR 3.35, P < 0.001), but 42 (50%) still received oxycodone. Patients with oxycodone allergies and intolerances had higher odds of elevated subjective pain (OR 1.60, P = 0.013; OR 2.36, P = 0.002, respectively) and longer length of stay (OR 1.36, P = 0.038; OR 2.24, P = 0.002, respectively) than patients without these. Conclusions: General surgery patients with oxycodone allergies and intolerances are at greater risk of worse postoperative pain and longer length of stay, compared to patients without. Many still receive oxycodone, and other opioids that could cause cross‐reactivity. [ABSTRACT FROM AUTHOR]

Background: 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. Methods: A retrospective cohort study was conducted including patients admitted under general surgical services of two hospitals in South Australia over 2 years. Likelihood ratios for patients meeting MER activation criteria, or a vital sign in the most extreme 1% for general surgery inpatients (<0.5th percentile or > 99.5th percentile), were calculated to predict in‐hospital mortality. Results: 15 969 inpatient admissions were included comprising 2 254 617 total vital sign observations. The 0.5th and 99.5th centile for heart rate was 48 and 133, systolic blood pressure 85 and 184, respiratory rate 10 and 31, and oxygen saturations 89% and 100%, respectively. MER activation criteria with the highest positive likelihood ratio for in‐hospital mortality were heart rate ≤ 39 (37.65, 95% CI 27.71–49.51), respiratory rate ≥ 31 (15.79, 95% CI 12.82–19.07), and respiratory rate ≤ 7 (10.53, 95% CI 6.79–14.84). These MER activation criteria likelihood ratios were similar to those derived when applying a threshold of the most extreme 1% of vital signs. Conclusions: This study demonstrated that vital signs within Australian guidelines, and escalation to MER activation, appropriately predict in‐hospital mortality in a large cohort of patients admitted to general surgical services in South Australia. [ABSTRACT FROM AUTHOR]

Background: This study aimed to examine the performance of machine learning algorithms for the prediction of discharge within 12 and 24 h to produce a measure of readiness for discharge after general surgery. Methods: Consecutive general surgery patients at two tertiary hospitals, over a 2‐year period, were included. Observation and laboratory parameter data were stratified into training, testing and validation datasets. Random forest, XGBoost and logistic regression models were evaluated. Each ward round note time was taken as a different event. Primary outcome was classification accuracy of the algorithmic model able to predict discharge within the next 12 h on the validation data set. Results: 42 572 ward round note timings were included from 8826 general surgery patients. Discharge occurred within 12 h for 8800 times (20.7%), and within 24 h for 9885 (23.2%). For predicting discharge within 12 h, model classification accuracies for derivation and validation data sets were: 0.84 and 0.85 random forest, 0.84 and 0.83 XGBoost, 0.80 and 0.81 logistic regression. For predicting discharge within 24 h, model classification accuracies for derivation and validation data sets were: 0.83 and 0.84 random forest, 0.82 and 0.81 XGBoost, 0.78 and 0.79 logistic regression. Algorithms generated a continuous number between 0 and 1 (or 0 and 100), representing readiness for discharge after general surgery. Conclusions: A derived artificial intelligence measure (the Adelaide Score) successfully predicts discharge within the next 12 and 24 h in general surgery patients. This may be useful for both treating teams and allied health staff within surgical systems. [ABSTRACT FROM AUTHOR]