Fluid migration pathways to groundwater in mature oil fields: Exploring the roles of water injection/production and oil-well integrity in California, USA.

Mature oil fields potentially contain multiple fluid migration pathways toward protected groundwater (total dissolved solids, TDS, in nonexempted aquifer <10,000 mg/L) because of their extensive development histories. Time-series data for water use, fluid pressures, oil-well construction, and geochemistry from the South Belridge and Lost Hills mature oil fields in California are used to explore the roles of injection/production of oil-field water and well-integrity issues in fluid migration. Injection/production of oil-field water modified hydraulic gradients in both oil fields, resulting in chemical transport from deeper groundwater and hydrocarbon-reservoir systems to aquifers in the oil fields. Those aquifers are used for water supply outside the oil-field boundaries. Oil wells drilled before 1976 can be fluid migration pathways because a relatively large percentage of them have >10 m of uncemented annulus that straddles oil-well casing damage and/or the base of groundwater with TDS <10,000 mg/L. The risk of groundwater-quality degradation is higher when wells with those risk factors occur in areas with upward hydraulic gradients created by positive net injection, groundwater withdrawals, or combinations of these variables. The complex changes in hydrologic conditions and groundwater chemistry likely would not have been discovered in the absence of years to decades of monitoring data for groundwater elevations and chemistry, and installation of monitoring wells in areas with overlapping risk factors. Important monitoring concepts based on results from this and other studies include monitoring hydrocarbon-reservoir and groundwater systems at multiple spatiotemporal scales and maintaining transparency and accessibility of data and analyses. This analysis focuses on two California oil fields, but the methods used and processes affecting fluid migration could be relevant in other oil fields where substantial injection/production of oil-field water occurs and oil-well integrity is of concern.

Groundwater Quality of Aquifers Overlying the Oxnard Oil Field, Ventura County, California.

Groundwater samples collected from irrigation, monitoring, and municipal supply wells near the Oxnard Oil Field were analyzed for chemical and isotopic tracers to evaluate if thermogenic gas or water from hydrocarbon-bearing formations have mixed with surrounding groundwater. New and historical data show no evidence of water from hydrocarbon-bearing formations in groundwater overlying the field. However, thermogenic gas mixed with microbial methane was detected in 5 wells at concentrations ranging from 0.011-9.1 mg/L. The presence of these gases at concentrations <10 mg/L do not indicate degraded water quality posing a known health risk. Analysis of carbon isotopes (δ13C-CH4) and hydrogen isotopes (δ2H-CH4) of methane and ratios of methane to heavier hydrocarbon gases were used to differentiate sources of methane between a) microbial, b) thermogenic or c) mixed sources. Results indicate that microbial-sourced methane is widespread in the study area, and concentrations overlap with those from thermogenic sources. The highest concentrations of thermogenic gas were observed in proximity to relatively high density of oil wells, large injection volumes of water disposal and cyclic steam, shallow oil development, and hydrocarbon shows in sediments overlying the producing oil reservoirs. Depths of water wells containing thermogenic gas were within approximately 200 m of the top of the Vaca Tar Sand production zone (approximately 600 m below land surface). Due to the limited sampling density, the source and pathways of thermogenic gas detected in groundwater could not be conclusively determined. Thermogenic gas detected in the absence of co-occurring water from hydrocarbon-bearing formations may result from natural gas migration over geologic time from the Vaca Tar Sand or deeper formations, hydrocarbon shows in sediments overlying producing zones, and/or gas leaking from oil-field infrastructure. Denser sampling of groundwater, potential end-members, and pressure monitoring could help better distinguish pathways of thermogenic gases.

Multicenter validation of a machine-learning algorithm for 48-h all-cause mortality prediction.

In order to evaluate mortality predictions based on boosted trees, this retrospective study uses electronic medical record data from three academic health centers for inpatients 18 years or older with at least one observation of each vital sign. Predictions were made 12, 24, and 48 hours before death. Models fit to training data from each institution were evaluated using hold-out test data from the same institution, and from the other institutions. Gradient-boosted trees (GBT) were compared to regularized logistic regression (LR) predictions, support vector machine (SVM) predictions, quick Sepsis-Related Organ Failure Assessment (qSOFA), and Modified Early Warning Score (MEWS) using area under the receiver operating characteristic curve (AUROC). For training and testing GBT on data from the same institution, the average AUROCs were 0.96, 0.95, and 0.94 across institutional test sets for 12-, 24-, and 48-hour predictions, respectively. When trained and tested on data from different hospitals, GBT AUROCs achieved up to 0.98, 0.96, and 0.96, for 12-, 24-, and 48-hour predictions, respectively. Average AUROC for 48-hour predictions for LR, SVM, MEWS, and qSOFA were 0.85, 0.79, 0.86 and 0.82, respectively. GBT predictions may help identify patients who would benefit from increased clinical care.

Multicentre validation of a sepsis prediction algorithm using only vital sign data in the emergency department, general ward and ICU.

OBJECTIVES: We validate a machine learning-based sepsis-prediction algorithm (InSight) for the detection and prediction of three sepsis-related gold standards, using only six vital signs. We evaluate robustness to missing data, customisation to site-specific data using transfer learning and generalisability to new settings. DESIGN: A machine-learning algorithm with gradient tree boosting. Features for prediction were created from combinations of six vital sign measurements and their changes over time. SETTING: A mixed-ward retrospective dataset from the University of California, San Francisco (UCSF) Medical Center (San Francisco, California, USA) as the primary source, an intensive care unit dataset from the Beth Israel Deaconess Medical Center (Boston, Massachusetts, USA) as a transfer-learning source and four additional institutions' datasets to evaluate generalisability. PARTICIPANTS: 684 443 total encounters, with 90 353 encounters from June 2011 to March 2016 at UCSF. INTERVENTIONS: None. PRIMARY AND SECONDARY OUTCOME MEASURES: Area under the receiver operating characteristic (AUROC) curve for detection and prediction of sepsis, severe sepsis and septic shock. RESULTS: For detection of sepsis and severe sepsis, InSight achieves an AUROC curve of 0.92 (95% CI 0.90 to 0.93) and 0.87 (95% CI 0.86 to 0.88), respectively. Four hours before onset, InSight predicts septic shock with an AUROC of 0.96 (95% CI 0.94 to 0.98) and severe sepsis with an AUROC of 0.85 (95% CI 0.79 to 0.91). CONCLUSIONS: InSight outperforms existing sepsis scoring systems in identifying and predicting sepsis, severe sepsis and septic shock. This is the first sepsis screening system to exceed an AUROC of 0.90 using only vital sign inputs. InSight is robust to missing data, can be customised to novel hospital data using a small fraction of site data and retains strong discrimination across all institutions.

Relationship Between a Sepsis Intervention Bundle and In-Hospital Mortality Among Hospitalized Patients: A Retrospective Analysis of Real-World Data.

BACKGROUND: Sepsis is a systemic response to infection that can lead to tissue damage, organ failure, and death. Efforts have been made to develop evidence-based intervention bundles to identify and manage sepsis early in the course of the disease to decrease sepsis-related morbidity and mortality. We evaluated the relationship between a minimally invasive sepsis intervention bundle and in-hospital mortality using robust methods for observational data. METHODS: We performed a retrospective cohort study at the University of California, San Francisco, Medical Center among adult patients discharged between January 1, 2012, and December 31, 2014, and who received a diagnosis of severe sepsis/septic shock (SS/SS). Sepsis intervention bundle elements included measurement of blood lactate; drawing of blood cultures before starting antibiotics; initiation of broad spectrum antibiotics within 3 hours of sepsis presentation in the emergency department or 1 hour of presentation on an inpatient unit; administration of intravenous fluid bolus if the patient was hypotensive or had a lactate level >4 mmol/L; and starting intravenous vasopressors if the patient remained hypotensive after fluid bolus administration. Poisson regression for a binary outcome variable was used to estimate an adjusted incidence-rate ratio (IRR) comparing mortality in groups defined by bundle compliance measured as a binary predictor, and to estimate an adjusted number needed to treat (NNT). RESULTS: Complete bundle compliance was associated with a 31% lower risk of mortality (adjusted IRR, 0.69, 95% confidence interval [CI], 0.53-0.91), adjusting for SS/SS presentation in the emergency department, SS/SS present on admission (POA), age, admission severity of illness and risk of mortality, Medicaid/Medicare payor status, immunocompromised host status, and congestive heart failure POA. The adjusted NNT to save one life was 15 (CI, 8-69). Other factors independently associated with mortality included SS/SS POA (adjusted IRR, 0.55; CI, 0.32-0.92) and increased age (adjusted IRR, 1.13 per 10-year increase in age; CI, 1.03-1.24). CONCLUSIONS: The University of California, San Francisco, sepsis bundle was associated with a decreased risk of in-hospital mortality across hospital units after robust control for confounders and risk adjustment. The adjusted NNT provides a reasonable and achievable goal to observe measureable improvements in outcomes for patients diagnosed with SS/SS.

Cost and mortality impact of an algorithm-driven sepsis prediction system.

AIMS: To compute the financial and mortality impact of InSight, an algorithm-driven biomarker, which forecasts the onset of sepsis with minimal use of electronic health record data. METHODS: This study compares InSight with existing sepsis screening tools and computes the differential life and cost savings associated with its use in the inpatient setting. To do so, mortality reduction is obtained from an increase in the number of sepsis cases correctly identified by InSight. Early sepsis detection by InSight is also associated with a reduction in length-of-stay, from which cost savings are directly computed. RESULTS: InSight identifies more true positive cases of severe sepsis, with fewer false alarms, than comparable methods. For an individual ICU with 50 beds, for example, it is determined that InSight annually saves 75 additional lives and reduces sepsis-related costs by $560,000. LIMITATIONS: InSight performance results are derived from analysis of a single-center cohort. Mortality reduction results rely on a simplified use case, which fixes prediction times at 0, 1, and 2 h before sepsis onset, likely leading to under-estimates of lives saved. The corresponding cost reduction numbers are based on national averages for daily patient length-of-stay cost. CONCLUSIONS: InSight has the potential to reduce sepsis-related deaths and to lead to substantial cost savings for healthcare facilities.

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

INTRODUCTION: Several 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. METHODS: We 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. RESULTS: Outcomes 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. CONCLUSION: The 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 REGISTRATION: NCT03015454.

Prediction of Sepsis in the Intensive Care Unit With Minimal Electronic Health Record Data: A Machine Learning Approach.

BACKGROUND: Sepsis is one of the leading causes of mortality in hospitalized patients. Despite this fact, a reliable means of predicting sepsis onset remains elusive. Early and accurate sepsis onset predictions could allow more aggressive and targeted therapy while maintaining antimicrobial stewardship. Existing detection methods suffer from low performance and often require time-consuming laboratory test results. OBJECTIVE: To study and validate a sepsis prediction method, InSight, for the new Sepsis-3 definitions in retrospective data, make predictions using a minimal set of variables from within the electronic health record data, compare the performance of this approach with existing scoring systems, and investigate the effects of data sparsity on InSight performance. METHODS: We apply InSight, a machine learning classification system that uses multivariable combinations of easily obtained patient data (vitals, peripheral capillary oxygen saturation, Glasgow Coma Score, and age), to predict sepsis using the retrospective Multiparameter Intelligent Monitoring in Intensive Care (MIMIC)-III dataset, restricted to intensive care unit (ICU) patients aged 15 years or more. Following the Sepsis-3 definitions of the sepsis syndrome, we compare the classification performance of InSight versus quick sequential organ failure assessment (qSOFA), modified early warning score (MEWS), systemic inflammatory response syndrome (SIRS), simplified acute physiology score (SAPS) II, and sequential organ failure assessment (SOFA) to determine whether or not patients will become septic at a fixed period of time before onset. We also test the robustness of the InSight system to random deletion of individual input observations. RESULTS: In a test dataset with 11.3% sepsis prevalence, InSight produced superior classification performance compared with the alternative scores as measured by area under the receiver operating characteristic curves (AUROC) and area under precision-recall curves (APR). In detection of sepsis onset, InSight attains AUROC = 0.880 (SD 0.006) at onset time and APR = 0.595 (SD 0.016), both of which are superior to the performance attained by SIRS (AUROC: 0.609; APR: 0.160), qSOFA (AUROC: 0.772; APR: 0.277), and MEWS (AUROC: 0.803; APR: 0.327) computed concurrently, as well as SAPS II (AUROC: 0.700; APR: 0.225) and SOFA (AUROC: 0.725; APR: 0.284) computed at admission (P<.001 for all comparisons). Similar results are observed for 1-4 hours preceding sepsis onset. In experiments where approximately 60% of input data are deleted at random, InSight attains an AUROC of 0.781 (SD 0.013) and APR of 0.401 (SD 0.015) at sepsis onset time. Even with 60% of data missing, InSight remains superior to the corresponding SIRS scores (AUROC and APR, P<.001), qSOFA scores (P=.0095; P<.001) and superior to SOFA and SAPS II computed at admission (AUROC and APR, P<.001), where all of these comparison scores (except InSight) are computed without data deletion. CONCLUSIONS: Despite using little more than vitals, InSight is an effective tool for predicting sepsis onset and performs well even with randomly missing data.

Fresh and stored red blood cell transfusion equivalently induce subclinical pulmonary gas exchange deficit in normal humans.

BACKGROUND: Transfusion can cause severe acute lung injury, although most transfusions do not seem to induce complications. We tested the hypothesis that transfusion can cause mild pulmonary dysfunction that has not been noticed clinically and is not sufficiently severe to fit the definition of transfusion-related acute lung injury. METHODS: We studied 35 healthy, normal volunteers who donated 1 U of blood 4 weeks and another 3 weeks before 2 study days separated by 1 week. On study days, 2 U of blood were withdrawn while maintaining isovolemia, followed by transfusion with either the volunteer's autologous fresh red blood cells (RBCs) removed 2 hours earlier or their autologous stored RBCs (random order). The following week, each volunteer was studied again, transfused with the RBCs of the other storage duration. The primary outcome variable was the change in alveolar to arterial difference in oxygen partial pressure (AaDo(2)) from before to 60 minutes after transfusion with fresh or older RBCs. RESULTS: Fresh RBCs and RBCs stored for 24.5 days equally (P = 0.85) caused an increase of AaDo(2) (fresh: 2.8 mm Hg [95% confidence interval: 0.8-4.8; P = 0.007]; stored: 3.0 mm Hg [1.4-4.7; P = 0.0006]). Concentrations of all measured cytokines, except for interleukin-10 (P = 0.15), were less in stored leukoreduced (LR) than stored non-LR packed RBCs; however, vascular endothelial growth factor was the only measured in vivo cytokine that increased more after transfusion with LR than non-LR stored packed RBCs. Vascular endothelial growth factor was the only cytokine tested with in vivo concentrations that correlated with AaDo(2). CONCLUSION: RBC transfusion causes subtle pulmonary dysfunction, as evidenced by impaired gas exchange for oxygen, supporting our hypothesis that lung impairment after transfusion includes a wide spectrum of physiologic derangements and may not require an existing state of altered physiology. These data do not support the hypothesis that transfusion of RBCs stored for >21 days is more injurious than that of fresh RBCs.

Special article: rescue therapies for acute hypoxemic respiratory failure.

The recent H1N1 epidemic has resulted in a large number of deaths, primarily from acute hypoxemic respiratory failure. We reviewed the current strategies to rescue patients with severe hypoxemia. Included in these strategies are high-frequency oscillatory ventilation, airway pressure release ventilation, inhaled vasodilators, and the use of extracorporeal life support. All of these strategies are targeted at improving oxygenation, but improved oxygenation alone has yet to be demonstrated to correlate with improved survival. The risks and benefits of these strategies, including cost-effectiveness data, are discussed.

Effects of monoclonal anti-PcrV antibody on Pseudomonas aeruginosa-induced acute lung injury in a rat model.

BACKGROUND: The effects of the murine monoclonal anti-PcrV antibody Mab166 on acute lung injury induced by Pseudomonas aeruginosa were analyzed in a rat model. METHODS: Lung injury was induced by the instillation of P. aeruginosa strain PA103 directly into the left lungs of anesthetized rats. One hour after the bacterial instillation, rabbit polyclonal anti-PcrV IgG, murine monoclonal anti-PcrV IgG Mab166 or Mab166 Fab-fragments were administered intratracheally directly into the lungs. The degree of alveolar epithelial injury, amount of lung edema, decrease in oxygenation and extent of lung inflammation by histology were evaluated as independent parameters of acute lung injury. RESULTS: These parameters improved in rats that had received intratracheal instillation of either rabbit polyclonal anti-PcrV IgG, murine monoclonal anti-PcrV IgG Mab166 or Mab166 Fab-fragments in comparison with the control group. CONCLUSION: Mab166 and its Fab fragments have potential as adjuvant therapy for acute lung injury due to P. aeruginosa pneumonia.

Injury and repair in lung and airways.

Acute lung injury (ALI) and acute respiratory distress syndrome (ARDS) are common causes of morbidity and mortality in the intensive care unit. ALI/ARDS occurs as a result of systemic inflammation, usually triggered by a microorganism. Activation of leukocytes and release of proinflammatory mediators from multiple cellular sources result in both local and distant tissue injury. Tumor necrosis factor-alpha and interleukin-1 beta are the best characterized of the proinflammatory cytokines contributing to ALI/ARDS and subsequent fibrosis. The ultimate clinical course of ALI/ARDS often is determined by the ability of the injured lung to repopulate the alveolar epithelium with functional cells. Death may occur when fibrosis predominates the healing response, as it results in worsening lung compliance and oxygenation. The rodent bleomycin model of lung fibrosis allows the use of molecular tools to dissect the cellular and subcellular processes leading to fibrosis. The elements of this response may provide therapeutic targets for the prevention of this devastating complication of ALI/ARDS.

O-antigen serotypes and type III secretory toxins in clinical isolates of Pseudomonas aeruginosa.

The association of O-antigen serotypes with type III secretory toxins was analyzed in 99 clinical isolates of Pseudomonas aeruginosa. Isolates secreting ExoU were frequently serotyped as O11, but none were serotype O1. Most of the isolates that were nontypeable for O antigen did not secrete type III secretory toxins.