Effect of a machine learning-based severe sepsis prediction algorithm on patient survival and hospital length of stay: a randomised clinical trial.

IntroductionSeveral methods have been developed to electronically monitor patients for severe sepsis, but few provide predictive capabilities to enable early intervention; furthermore, no severe sepsis prediction systems have been previously validated in a randomised study. We tested the use of a machine learning-based severe sepsis prediction system for reductions in average length of stay and in-hospital mortality rate.MethodsWe conducted a randomised controlled clinical trial at two medical-surgical intensive care units at the University of California, San Francisco Medical Center, evaluating the primary outcome of average length of stay, and secondary outcome of in-hospital mortality rate from December 2016 to February 2017. Adult patients (18+) admitted to participating units were eligible for this factorial, open-label study. Enrolled patients were assigned to a trial arm by a random allocation sequence. In the control group, only the current severe sepsis detector was used; in the experimental group, the machine learning algorithm (MLA) was also used. On receiving an alert, the care team evaluated the patient and initiated the severe sepsis bundle, if appropriate. Although participants were randomly assigned to a trial arm, group assignments were automatically revealed for any patients who received MLA alerts.ResultsOutcomes from 75 patients in the control and 67 patients in the experimental group were analysed. Average length of stay decreased from 13.0 days in the control to 10.3 days in the experimental group (p=0.042). In-hospital mortality decreased by 12.4 percentage points when using the MLA (p=0.018), a relative reduction of 58.0%. No adverse events were reported during this trial.ConclusionThe MLA was associated with improved patient outcomes. This is the first randomised controlled trial of a sepsis surveillance system to demonstrate statistically significant differences in length of stay and in-hospital mortality.Trial registrationNCT03015454

Supervised machine learning for the early prediction of acute respiratory distress syndrome (ARDS).

PURPOSE: Acute respiratory distress syndrome (ARDS) is a serious respiratory condition with high mortality and associated morbidity. The objective of this study is to develop and evaluate a novel application of gradient boosted tree models trained on patient health record data for the early prediction of ARDS. MATERIALS AND METHODS: 9919 patient encounters were retrospectively analyzed from the Medical Information Mart for Intensive Care III (MIMIC-III) data base. XGBoost gradient boosted tree models for early ARDS prediction were created using routinely collected clinical variables and numerical representations of radiology reports as inputs. XGBoost models were iteratively trained and validated using 10-fold cross validation. RESULTS: On a hold-out test set, algorithm classifiers attained area under the receiver operating characteristic curve (AUROC) values of 0.905 when tested for the detection of ARDS at onset and 0.827, 0.810, and 0.790 for the prediction of ARDS at 12-, 24-, and 48-h windows prior to onset, respectively. CONCLUSION: Supervised machine learning predictions may help predict patients with ARDS up to 48 h prior to onset

Energy landscapes for a machine learning application to series data

Methods developed to explore and characterise potential energy landscapes are applied to the corresponding landscapes obtained from optimisation of a cost function in machine learning. We consider neural network predictions for the outcome of local geometry optimisation in a triatomic cluster, where four distinct local minima exist. The accuracy of the predictions is compared for fits using data from single and multiple points in the series of atomic configurations resulting from local geometry optimisation and for alternative neural networks. The machine learning solution landscapes are visualised using disconnectivity graphs, and signatures in the effective heat capacity are analysed in terms of distributions of local minima and their properties.We gratefully acknowledge funding from the EPSRC and the ERC

Machine-Learning-Based Laboratory Developed Test for the Diagnosis of Sepsis in High-Risk Patients

Sepsis, a dysregulated host response to infection, is a major health burden in terms of both mortality and cost. The difficulties clinicians face in diagnosing sepsis, alongside the insufficiencies of diagnostic biomarkers, motivate the present study. This work develops a machine-learning-based sepsis diagnostic for a high-risk patient group, using a geographically and institutionally diverse collection of nearly 500,000 patient health records. Using only a minimal set of clinical variables, our diagnostics outperform common severity scoring systems and sepsis biomarkers and benefit from being available immediately upon ordering

Intra hepatic biliary mucinous cyst adenoma (BMCA): varied presentation and management options

Benign cystic neoplasms of liver comprises of 5-10% of hepatic lesions. While simple cysts are common, biliary mucinous cystic tumors (BMCT) are rare tumors. We report various presentations of benign mucinous cystadenoma (BMCA), ranging from a small well defined cystic lesion to large lesions replacing one lobe of liver or involving multiple sectors of the liver, requiring a wide  range of surgical procedures to treat such tumors. Histopathology confirmed the diagnosis of benign biliary cystic neoplasm in all the cases. Rare occurrence, potentially malignant nature of the disease and difficulty in differentiating from other cystic hepatic lesions need careful evaluation of such cases to select the optimum management option. Keywords: Intrahepatic biliary mucinous cystic neoplasm, biliary mucinous cystadenocarcinoma, cystic hepatic tumor, biliary cystadenoma

Research data supporting "Energy Landscapes for a Machine Learning Application to Series Data"

time series data used for training and testing neural netsThis work was supported by the ERC [grant number MAAG/837 RG59508] and EPSRC [grant number MAAG/837 RG59508]

Gastrointestinal Stromal Tumors (GIST): Is the Incidence rising in India? —A Hospital Based Analysis

Context: Gastrointestinal stromal tumours (GISTs) are rare tumours of the gastrointestinal tract (GI) but they are the most common amongst the mesenchymal tumours. However, there are very few published articles on patients with the diagnoses of GISTs from the Indian subcontinent and particularly from the eastern part of India. Also we noted an increased number of patients with the diagnosis of GISTs in our clinical practice compared to the past decade and have observed an increased incidence of tumours arising from the small bowel and large bowel compared to the stomach. Aims: To study the incidence of symptomatic GISTs, the demographic details, clinical presentations, the histopathological and immunohistochemistry features and survival of the patients and response of these tumours to imatinib therapy. Settings and Design: A retrospective study based on hospital registry conducted in the Departments of Radiotherapy and General Surgery, IPGME&R- SSKM Hospital, Kolkata and NRSMCH, Kolkata. Methods & Material: Cross sectional imaging and endoscopic evaluations were used to diagnose the tumours. Tumor categorization required microscopic and immunohistochemistry studies for c-Kit, DOG-1 and other tumor markers. High risk group tumours were treated with imatinib 400 mg/day for 3 years duration. Statistical Analysis:Incidence of GISTs was analyzed using Pearson Chi-square test and Survival was analyzed using Kaplan-Meier survival curve and Pearson Chi-square test. Results: Incidence of GISTs in 2010-2011 was 0.37% whereas in 2018-2019 it was 2.48% with 85% increase, p value of <0.001. The commonest tumor location was in the small bowel (40.7%), followed by stomach (25.4%) and colo-rectum (10.2%). Mean duration of imatinib therapy was 19.33 months with 84% overall survival. Estimated three-year OS (overall survival) was 73.6%. Estimated mean OS was 66±5.39 months with 95% CI 55.6-76.7. Mean survival of patients with metastatic disease on imatinib therapy was 16.88 months with p=0.000. Primary response to imatinib therapy was observed in 93.87% (43/46) patients. Patients developing disease progression on imatinib were treated with Sunitinib and they demonstrated partial response. Conclusion: We have documented an increased incidence of gastrointestinal stromal tumours and there is increased proportion of small bowel and colorectal tumours compared to gastric tumours. Keywords: Gastrointestinal Stromal Tumours, Incidence, cKIT, DOG-1, Imatinib

Prediction of Acute Kidney Injury With a Machine Learning Algorithm Using Electronic Health Record Data

Background: A major problem in treating acute kidney injury (AKI) is that clinical criteria for recognition are markers of established kidney damage or impaired function; treatment before such damage manifests is desirable. Clinicians could intervene during what may be a crucial stage for preventing permanent kidney injury if patients with incipient AKI and those at high risk of developing AKI could be identified. Objective: In this study, we evaluate a machine learning algorithm for early detection and prediction of AKI. Design: We used a machine learning technique, boosted ensembles of decision trees, to train an AKI prediction tool on retrospective data taken from more than 300 000 inpatient encounters. Setting: Data were collected from inpatient wards at Stanford Medical Center and intensive care unit patients at Beth Israel Deaconess Medical Center. Patients: Patients older than the age of 18 whose hospital stays lasted between 5 and 1000 hours and who had at least one documented measurement of heart rate, respiratory rate, temperature, serum creatinine (SCr), and Glasgow Coma Scale (GCS). Measurements: We tested the algorithm’s ability to detect AKI at onset and to predict AKI 12, 24, 48, and 72 hours before onset. Methods: We tested AKI detection and prediction using the National Health Service (NHS) England AKI Algorithm as a gold standard. We additionally tested the algorithm’s ability to detect AKI as defined by the Kidney Disease: Improving Global Outcomes (KDIGO) guidelines. We compared the algorithm’s 3-fold cross-validation performance to the Sequential Organ Failure Assessment (SOFA) score for AKI identification in terms of area under the receiver operating characteristic (AUROC). Results: The algorithm demonstrated high AUROC for detecting and predicting NHS-defined AKI at all tested time points. The algorithm achieves AUROC of 0.872 (95% confidence interval [CI], 0.867-0.878) for AKI detection at time of onset. For prediction 12 hours before onset, the algorithm achieves an AUROC of 0.800 (95% CI, 0.792-0.809). For 24-hour predictions, the algorithm achieves AUROC of 0.795 (95% CI, 0.785-0.804). For 48-hour and 72-hour predictions, the algorithm achieves AUROC values of 0.761 (95% CI, 0.753-0.768) and 0.728 (95% CI, 0.719-0.737), respectively. Limitations: Because of the retrospective nature of this study, we cannot draw any conclusions about the impact the algorithm’s predictions will have on patient outcomes in a clinical setting. Conclusions: The results of these experiments suggest that a machine learning–based AKI prediction tool may offer important prognostic capabilities for determining which patients are likely to suffer AKI, potentially allowing clinicians to intervene before kidney damage manifests