Testing prediction accuracy of ideal and prolonged length of hospital stay following ovarian cancer cytoreduction using machine learning methods

Introduction/Backgroun Cytoreductive surgery for advanced high grade serous ovarian cancer (HGSOC) patients to achieve complete removal of all visible disease often requires prolonged surgical time and possible multi-visceral resection potentially necessitating HDU support and prolonged hospitalisation. Length of stay (LOS) has been suggested as a marker of quality or effectiveness of short-term care. Identifying modifiable risk factors at admission predicting LOS could lead to appropriately targeted interventions to improve the delivery of care. Modern data mining technologies such as Machine Learning (ML) could be helpful in monitoring hospital stays to improve standards of care. We aimed to improve the accuracy of predicting both ideal and prolonged LOS, by use of ML algorithms. Methodology A cohort of 176 HGSOC patients, who underwent surgical cytoreduction, from Jan 2014 to Dec 2017 was selected from the ovarian database. They were randomly assigned to ‘training’ and ‘test’ subcohorts. ML methods including Linear Discriminant Analysis (LDA), Support Vector Machine (SVM), Decision-Tree-Analysis, and K-Nearest Neighbors were employed to derive predictive information for LOS from selected variables including age, BMI, Surgical Complexity Score (SCS), operative time, preoperative albumin and morbidity score (Clavien-Dindo 3–5). These methods were tested against conventional linear regression. The accepted ‘ideal’ LOS was deemed to be 5 days or fewer. Prolonged LOS was defined as time spent in the hospital beyond the 90th percentile. Through the introduction of the Enhanced Recovery after Surgery (ERAS) pathway in 2015, effort was made to shorten the LOS for patients following major surgery, whilst still assuring they received effective treatment and high-quality care. Results Mean and median LOS was 4.6 and 4.0 days (IQR 0–38), respectively. The delayed LOS group consisted those staying 10 days or longer. The rate of ideal LOS continuously improved for every year from 32% in 2016 to 73.5% in 2019 despite increasing mean SCS. For ideal LOS prediction accuracy, ML slightly outperformed conventional logistic regression, with no bowel resection and operative time been the most predictive variables. For prolonged LOS, LDA and SVM were more accurate to predict prolonged LOS than conventional regression. Bowel resection and Clavien-Dindo complications were most importantly contributing to the improved accuracy of the model (figure 1)

Testing prediction accuracy of hdu admission following high grade serous advanced ovarian cancer cytoreductive surgery using machine learning methods

Introduction/Background Advanced high grade serous ovarian cancer patients (HGSOC) frequently require extensive procedures including bowel resections and upper abdominal surgery potentially necessitating HDU/ICU support and prolonged hospitalisation. HDU/ICU admission is a measurable outcome that can be used as a benchmark of surgical care. Modern data mining technologies such as Machine Learning (ML), a subfield of Artificial Intelligence, could be helpful in monitoring HDU/ICU admissions to improve standards of care. We aimed to improve the accuracy of predicting HDU admission in that cohort of patients by use of ML algorithms. Methodology A cohort of 176 HGSOC patients, who underwent surgical cytoreduction from Jan 2014 to Dec 2017 was selected from the ovarian database. They were randomly assigned to ‘training’ and ‘test’ subcohorts. ML methods including Classification and Regression Trees (CART) and Support Vector Machine (SVM), were employed to derive predictive information for HDU/ICU admission from a list of selected preoperative, intraoperative, and postoperative variables. These methods were tested against conventional linear regression analyses. Results There were 29 out of 176 (16.4%) HDU/ICU admissions; 23 admissions were elective whilst six were unplanned admissions. For the outcome of HDU/ICU admission, both ML methods outperformed conventional regression by far (table 1). Bowel resection and operative time were the most predictive variables (figure 1). HDU/ICU admission was not associated with increased length of stay, increased number of postoperative complications, and increased risk of readmission within 30 days

Stratification of Length of Stay Prediction following Surgical Cytoreduction in Advanced High-Grade Serous Ovarian Cancer Patients Using Artificial Intelligence; the Leeds L-AI-OS Score

(1) Background: Length of stay (LOS) has been suggested as a marker of the effectiveness of short-term care. Artificial Intelligence (AI) technologies could help monitor hospital stays. We developed an AI-based novel predictive LOS score for advanced-stage high-grade serous ovarian cancer (HGSOC) patients following cytoreductive surgery and refined factors significantly affecting LOS. (2) Methods: Machine learning and deep learning methods using artificial neural networks (ANN) were used together with conventional logistic regression to predict continuous and binary LOS outcomes for HGSOC patients. The models were evaluated in a post-hoc internal validation set and a Graphical User Interface (GUI) was developed to demonstrate the clinical feasibility of sophisticated LOS predictions. (3) Results: For binary LOS predictions at differential time points, the accuracy ranged between 70–98%. Feature selection identified surgical complexity, pre-surgery albumin, blood loss, operative time, bowel resection with stoma formation, and severe postoperative complications (CD3–5) as independent LOS predictors. For the GUI numerical LOS score, the ANN model was a good estimator for the standard deviation of the LOS distribution by ± two days. (4) Conclusions: We demonstrated the development and application of both quantitative and qualitative AI models to predict LOS in advanced-stage EOC patients following their cytoreduction. Accurate identification of potentially modifiable factors delaying hospital discharge can further inform services performing root cause analysis of LOS

Using trained dogs and organic semi-conducting sensors to identify asymptomatic and mild SARS-CoV-2 infections: an observational study

Background A rapid, accurate, non-invasive diagnostic screen is needed to identify people with SARS-CoV-2 infection. We investigated whether organic semi-conducting (OSC) sensors and trained dogs could distinguish between people infected with asymptomatic or mild symptoms, and uninfected individuals, and the impact of screening at ports-of-entry. Methods Odour samples were collected from adults, and SARS-CoV-2 infection status confirmed using RT-PCR. OSC sensors captured the volatile organic compound (VOC) profile of odour samples. Trained dogs were tested in a double-blind trial to determine their ability to detect differences in VOCs between infected and uninfected individuals, with sensitivity and specificity as the primary outcome. Mathematical modelling was used to investigate the impact of bio-detection dogs for screening. Results About, 3921 adults were enrolled in the study and odour samples collected from 1097 SARS-CoV-2 infected and 2031 uninfected individuals. OSC sensors were able to distinguish between SARS-CoV-2 infected individuals and uninfected, with sensitivity from 98% (95% CI 95–100) to 100% and specificity from 99% (95% CI 97–100) to 100%. Six dogs were able to distinguish between samples with sensitivity ranging from 82% (95% CI 76–87) to 94% (95% CI 89–98) and specificity ranging from 76% (95% CI 70–82) to 92% (95% CI 88–96). Mathematical modelling suggests that dog screening plus a confirmatory PCR test could detect up to 89% of SARS-CoV-2 infections, averting up to 2.2 times as much transmission compared to isolation of symptomatic individuals only. Conclusions People infected with SARS-CoV-2, with asymptomatic or mild symptoms, have a distinct odour that can be identified by sensors and trained dogs with a high degree of accuracy. Odour-based diagnostics using sensors and/or dogs may prove a rapid and effective tool for screening large numbers of people