INTRODUCTION:Most risk assessment tools assume that the impact of risk factors is linear and cumulative. Using novel machine-learning techniques, we sought to design an interactive, nonlinear risk ...calculator for Emergency Surgery (ES).
METHODS:All ES patients in the American College of Surgeons National Surgical Quality Improvement Program (ACS-NSQIP) 2007 to 2013 database were included (derivation cohort). Optimal Classification Trees (OCT) were leveraged to train machine-learning algorithms to predict postoperative mortality, morbidity, and 18 specific complications (eg, sepsis, surgical site infection). Unlike classic heuristics (eg, logistic regression), OCT is adaptive and reboots itself with each variable, thus accounting for nonlinear interactions among variables. An application Predictive OpTimal Trees in Emergency Surgery Risk (POTTER) was then designed as the algorithms’ interactive and user-friendly interface. POTTER performance was measured (c-statistic) using the 2014 ACS-NSQIP database (validation cohort) and compared with the American Society of Anesthesiologists (ASA), Emergency Surgery Score (ESS), and ACS-NSQIP calculators’ performance.
RESULTS:Based on 382,960 ES patients, comprehensive decision-making algorithms were derived, and POTTER was created where the providerʼs answer to a question interactively dictates the subsequent question. For any specific patient, the number of questions needed to predict mortality ranged from 4 to 11. The mortality c-statistic was 0.9162, higher than ASA (0.8743), ESS (0.8910), and ACS (0.8975). The morbidity c-statistics was similarly the highest (0.8414).
CONCLUSION:POTTER is a highly accurate and user-friendly ES risk calculator with the potential to continuously improve accuracy with ongoing machine-learning. POTTER might prove useful as a tool for bedside preoperative counseling of ES patients and families.
Summary Background Immobilisation predicts adverse outcomes in patients in the surgical intensive care unit (SICU). Attempts to mobilise critically ill patients early after surgery are frequently ...restricted, but we tested whether early mobilisation leads to improved mobility, decreased SICU length of stay, and increased functional independence of patients at hospital discharge. Methods We did a multicentre, international, parallel-group, assessor-blinded, randomised controlled trial in SICUs of five university hospitals in Austria (n=1), Germany (n=1), and the USA (n=3). Eligible patients (aged 18 years or older, who had been mechanically ventilated for <48 h, and were expected to require mechanical ventilation for ≥24 h) were randomly assigned (1:1) by use of a stratified block randomisation via restricted web platform to standard of care (control) or early, goal-directed mobilisation using an inter-professional approach of closed-loop communication and the SICU optimal mobilisation score (SOMS) algorithm (intervention), which describes patients’ mobilisation capacity on a numerical rating scale ranging from 0 (no mobilisation) to 4 (ambulation). We had three main outcomes hierarchically tested in a prespecified order: the mean SOMS level patients achieved during their SICU stay (primary outcome), and patient's length of stay on SICU and the mini-modified functional independence measure score (mmFIM) at hospital discharge (both secondary outcomes). This trial is registered with ClinicalTrials.gov ( NCT01363102 ). Findings Between July 1, 2011, and Nov 4, 2015, we randomly assigned 200 patients to receive standard treatment (control; n=96) or intervention (n=104). Intention-to-treat analysis showed that the intervention improved the mobilisation level (mean achieved SOMS 2·2 SD 1·0 in intervention group vs 1·5 0·8 in control group, p<0·0001), decreased SICU length of stay (mean 7 days SD 5–12 in intervention group vs 10 days 6–15 in control group, p=0·0054), and improved functional mobility at hospital discharge (mmFIM score 8 4–8 in intervention group vs 5 2–8 in control group, p=0·0002). More adverse events were reported in the intervention group (25 cases 2·8%) than in the control group (ten cases 0·8%); no serious adverse events were observed. Before hospital discharge 25 patients died (17 16% in the intervention group, eight 8% in the control group). 3 months after hospital discharge 36 patients died (21 22% in the intervention group, 15 17% in the control group). Interpretation Early, goal-directed mobilisation improved patient mobilisation throughout SICU admission, shortened patient length of stay in the SICU, and improved patients’ functional mobility at hospital discharge. Funding Jeffrey and Judy Buzen.
Bowel Necrosis in the Setting of COVID-19 Gartland, Rajshri M.; Velmahos, George C.
Journal of gastrointestinal surgery,
12/2020, Letnik:
24, Številka:
12
Journal Article
This study examined records of 2566 consecutive COVID-19 patients at five Massachusetts hospitals and sought to predict level-of-care requirements based on clinical and laboratory data. Several ...classification methods were applied and compared against standard pneumonia severity scores. The need for hospitalization, ICU care, and mechanical ventilation were predicted with a validation accuracy of 88%, 87%, and 86%, respectively. Pneumonia severity scores achieve respective accuracies of 73% and 74% for ICU care and ventilation. When predictions are limited to patients with more complex disease, the accuracy of the ICU and ventilation prediction models achieved accuracy of 83% and 82%, respectively. Vital signs, age, BMI, dyspnea, and comorbidities were the most important predictors of hospitalization. Opacities on chest imaging, age, admission vital signs and symptoms, male gender, admission laboratory results, and diabetes were the most important risk factors for ICU admission and mechanical ventilation. The factors identified collectively form a signature of the novel COVID-19 disease.
Acute appendicitis (AA) is among the most common causes of acute abdominal pain. Diagnosis of AA is still challenging and some controversies on its management are still present among different ...settings and practice patterns worldwide. In July 2015, the World Society of Emergency Surgery (WSES) organized in Jerusalem the first consensus conference on the diagnosis and treatment of AA in adult patients with the intention of producing evidence-based guidelines. An updated consensus conference took place in Nijemegen in June 2019 and the guidelines have now been updated in order to provide evidence-based statements and recommendations in keeping with varying clinical practice: use of clinical scores and imaging in diagnosing AA, indications and timing for surgery, use of non-operative management and antibiotics, laparoscopy and surgical techniques, intra-operative scoring, and peri-operative antibiotic therapy.
This executive manuscript summarizes the WSES guidelines for the diagnosis and treatment of AA. Literature search has been updated up to 2019 and statements and recommendations have been developed according to the GRADE methodology. The statements were voted, eventually modified, and finally approved by the participants to the consensus conference and by the board of co-authors, using a Delphi methodology for voting whenever there was controversy on a statement or a recommendation. Several tables highlighting the research topics and questions, search syntaxes, and the statements and the WSES evidence-based recommendations are provided. Finally, two different practical clinical algorithms are provided in the form of a flow chart for both adults and pediatric (< 16 years old) patients.
The 2020 WSES guidelines on AA aim to provide updated evidence-based statements and recommendations on each of the following topics: (1) diagnosis, (2) non-operative management for uncomplicated AA, (3) timing of appendectomy and in-hospital delay, (4) surgical treatment, (5) intra-operative grading of AA, (6) ,management of perforated AA with phlegmon or abscess, and (7) peri-operative antibiotic therapy.
We sought to assess the performance of the Predictive OpTimal Trees in Emergency Surgery Risk (POTTER) tool in elderly emergency surgery (ES) patients.
The POTTER tool was derived using a novel ...Artificial Intelligence (AI)-methodology called optimal classification trees and validated for prediction of ES outcomes. POTTER outperforms all existent risk-prediction models and is available as an interactive smartphone application. Predicting outcomes in elderly patients has been historically challenging and POTTER has not yet been tested in this population.
All patients ≥65 years who underwent ES in the ACS-NSQIP 2017 database were included. POTTER's performance for 30-day mortality and 18 postoperative complications (eg, respiratory or renal failure) was assessed using c-statistic methodology, with planned sub-analyses for patients 65 to 74, 75 to 84, and 85+ years.
A total of 29,366 patients were included, with mean age 77, 55.8% females, and 62% who underwent emergency general surgery. POTTER predicted mortality accurately in all patients over 65 (c-statistic 0.80). Its best performance was in patients 65 to 74 years (c-statistic 0.84), and its worst in patients ≥85 years (c-statistic 0.71). POTTER had the best discrimination for predicting septic shock (c-statistic 0.90), respiratory failure requiring mechanical ventilation for ≥48 hours (c-statistic 0.86), and acute renal failure (c-statistic 0.85).
POTTER is a novel, interpretable, and highly accurate predictor of in-hospital mortality in elderly ES patients up to age 85 years. POTTER could prove useful for bedside counseling and for benchmarking of ES care.
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