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Large randomized trials in sepsis have generally failed to find effective novel treatments. This is increasingly attributed to patient heterogeneity, including heterogeneous cardiovascular changes in septic shock. We discuss the potential for machine learning systems to personalize cardiovascular resuscitation in sepsis. While the literature is replete with proofs of concept, the technological readiness of current systems is low, with a paucity of clinical trials and proven patient benefit. Systems may be vulnerable to confounding and poor generalization to new patient populations or contemporary patterns of care. Typical electronic health records do not capture rich enough data, at sufficient temporal resolution, to produce systems that make actionable treatment suggestions. To resolve these issues, we recommend a simultaneous focus on technical challenges and removing barriers to translation. This will involve improving data quality, adopting causally grounded models, prioritizing safety assessment and integration into healthcare workflows, conducting randomized clinical trials and aligning with regulatory requirements.

Objectives Analysis of routinely collected electronic health data is a key tool for long-term condition research and practice for hospitalised patients. This requires accurate and complete ascertainment of a broad range of diagnoses, something not always recorded on an admission document at a single point in time. This study aimed to ascertain how far back in time electronic hospital records need to be interrogated to capture long-term condition diagnoses.Design Retrospective observational study of routinely collected hospital electronic health record data.Setting Queen Elizabeth Hospital Birmingham (UK)-linked data held by the PIONEER acute care data hub.Participants Patients whose first recorded admission for chronic obstructive pulmonary disease (COPD) exacerbation (n=560) or acute stroke (n=2142) was between January and December 2018 and who had a minimum of 10 years of data prior to the index date.Outcome measures We identified the most common International Classification of Diseases version 10-coded diagnoses received by patients with COPD and acute stroke separately. For each diagnosis, we derived the number of patients with the diagnosis recorded at least once over the full 10-year lookback period, and then compared this with shorter lookback periods from 1 year to 9 years prior to the index admission.Results Seven of the top 10 most common diagnoses in the COPD dataset reached >90% completeness by 6 years of lookback. Atrial fibrillation and diabetes were >90% coded with 2–3 years of lookback, but hypertension and asthma completeness continued to rise all the way out to 10 years of lookback. For stroke, 4 of the top 10 reached 90% completeness by 5 years of lookback; angina pectoris was >90% coded at 7 years and previous transient ischaemic attack completeness continued to rise out to 10 years of lookback.Conclusion A 7-year lookback captures most, but not all, common diagnoses. Lookback duration should be tailored to the conditions being studied.

Background Improved access to healthcare in low- and middle-income countries (LMICs) has not equated to improved health outcomes. Absence or unsustained quality of care is partly to blame. Improving outcomes in intensive care units (ICUs) requires delivery of complex interventions by multiple specialties working in concert, and the simultaneous prevention of avoidable harms associated with the illness and the treatment interventions. Therefore, successful design and implementation of improvement interventions requires understanding of the behavioural, organisational, and external factors that determine care delivery and the likelihood of achieving sustained improvement. We aim to identify care processes that contribute to suboptimal clinical outcomes in ICUs located in LMICs and to establish barriers and enablers for improving the care processes. Methods Using rapid evaluation methods, we will use four data collection methods: 1) registry embedded indicators to assess quality of care processes and their associated outcomes; 2) process mapping to provide a preliminary framework to understand gaps between current and desired care practices; 3) structured observations of processes of interest identified from the process mapping and; 4) focus group discussions with stakeholders to identify barriers and enablers influencing the gap between current and desired care practices. We will also collect self-assessments of readiness for quality improvement. Data collection and analysis will be led by local stakeholders, performed in parallel and through an iterative process across eight countries: Kenya, India, Malaysia, Nepal, Pakistan, South Africa, Uganda and Vietnam. Conclusions The results of our study will provide essential information on where and how care processes can be improved to facilitate better quality of care to critically ill patients in LMICs; thus, reduce preventable mortality and morbidity in ICUs. Furthermore, understanding the rapid evaluation methods that will be used for this study will allow other researchers and healthcare professionals to carry out similar research in ICUs and other health services.

Background To determine the impact of the COVID-19 pandemic on the population with chronic Hepatitis B virus (HBV) infection under hospital follow-up in the UK, we quantified the coverage and frequency of measurements of biomarkers used for routine surveillance (alanine transferase [ALT] and HBV viral load). Methods We used anonymized electronic health record data from the National Institute for Health Research (NIHR) Health Informatics Collaborative (HIC) pipeline representing five UK National Health Service (NHS) Trusts. Results We report significant reductions in surveillance of both biomarkers during the pandemic compared to pre-COVID-19 years, both in terms of the proportion of patients who had ≥1 measurement annually, and the mean number of measurements per patient. Conclusions These results demonstrate the real-time utility of HIC data in monitoring health-care provision, and support interventions to provide catch-up services to minimise the impact of the pandemic. Further investigation is required to determine whether these disruptions will be associated with increased rates of adverse chronic HBV outcomes.

Abstract Machine learning for hospital operations is under-studied. We present a prediction pipeline that uses live electronic health-records for patients in a UK teaching hospital’s emergency department (ED) to generate short-term, probabilistic forecasts of emergency admissions. A set of XGBoost classifiers applied to 109,465 ED visits yielded AUROCs from 0.82 to 0.90 depending on elapsed visit-time at the point of prediction. Patient-level probabilities of admission were aggregated to forecast the number of admissions among current ED patients and, incorporating patients yet to arrive, total emergency admissions within specified time-windows. The pipeline gave a mean absolute error (MAE) of 4.0 admissions (mean percentage error of 17%) versus 6.5 (32%) for a benchmark metric. Models developed with 104,504 later visits during the Covid-19 pandemic gave AUROCs of 0.68–0.90 and MAE of 4.2 (30%) versus a 4.9 (33%) benchmark. We discuss how we surmounted challenges of designing and implementing models for real-time use, including temporal framing, data preparation, and changing operational conditions.

Abstract Background Faecal immunochemical tests (FITs) are used to triage primary care patients with symptoms that could be caused by colorectal cancer for referral to colonoscopy. The aim of this study was to determine whether combining FIT with routine blood test results could improve the performance of FIT in the primary care setting. Methods Results of all consecutive FITs requested by primary care providers between March 2017 and December 2020 were retrieved from the Oxford University Hospitals NHS Foundation Trust. Demographic factors (age, sex), reason for referral, and results of blood tests within 90 days were also retrieved. Patients were followed up for incident colorectal cancer in linked hospital records. The sensitivity, specificity, positive and negative predictive values of FIT alone, FIT paired with blood test results, and several multivariable FIT models, were compared. Results One hundred thirty-nine colorectal cancers were diagnosed (0.8%). Sensitivity and specificity of FIT alone at a threshold of 10 μg Hb/g were 92.1 and 91.5% respectively. Compared to FIT alone, blood test results did not improve the performance of FIT. Pairing blood test results with FIT increased specificity but decreased sensitivity. Multivariable models including blood tests performed similarly to FIT alone. Conclusions FIT is a highly sensitive tool for identifying higher risk individuals presenting to primary care with lower risk symptoms. Combining blood test results with FIT does not appear to lead to better discrimination for colorectal cancer than using FIT alone.

Background: A previous National Institute for Health and Care Research study [Harrison DA, Ferrando-Vivas P, Shahin J, Rowan KM. Ensuring comparisons of health-care providers are fair: development and validation of risk prediction models for critically ill patients. Health Serv Deliv Res 2015;3(41)] identified the need for more research to understand risk factors and consequences of critical care and subsequent outcomes. Objectives: First, to improve risk models for adult general critical care by developing models for mortality at fixed time points and time-to-event outcomes, end-stage renal disease, type 2 diabetes, health-care utilisation and costs. Second, to improve risk models for cardiothoracic critical care by enhancing risk factor data and developing models for longer-term mortality. Third, to improve risk models for in-hospital cardiac arrest by enhancing risk factor data and developing models for longer-term mortality and critical care utilisation. Design: Risk modelling study linking existing data. Setting: NHS adult critical care units and acute hospitals in England. Participants: Patients admitted to an adult critical care unit or experiencing an in-hospital cardiac arrest. Interventions: None. Main outcome measures: Mortality at hospital discharge, 30 days, 90 days and 1 year following critical care unit admission; mortality at 1 year following discharge from acute hospital; new diagnosis of end-stage renal disease or type 2 diabetes; hospital resource use and costs; return of spontaneous circulation sustained for > 20 minutes; survival to hospital discharge and 1 year; and length of stay in critical care following in-hospital cardiac arrest. Data sources: Case Mix Programme, National Cardiac Arrest Audit, UK Renal Registry, National Diabetes Audit, National Adult Cardiac Surgery Audit, Hospital Episode Statistics and Office for National Statistics. Results: Data were linked for 965,576 critical care admissions between 1 April 2009 and 31 March 2016, and 83,939 in-hospital cardiac arrests between 1 April 2011 and 31 March 2016. For admissions to adult critical care units, models for 30-day mortality had similar predictors and performance to those for hospital mortality and did not reduce heterogeneity. Models for longer-term outcomes reflected increasing importance of chronic over acute predictors. New models for end-stage renal disease and diabetes will allow benchmarking of critical care units against these important outcomes and identification of patients requiring enhanced follow-up. The strongest predictors of health-care costs were prior hospitalisation, prior dependency and chronic conditions. Adding pre- and intra-operative risk factors to models for cardiothoracic critical care gave little improvement in performance. Adding comorbidities to models for in-hospital cardiac arrest provided modest improvements but were of greater importance for longer-term outcomes. Limitations: Delays in obtaining linked data resulted in the data used being 5 years old at the point of publication: models will already require recalibration. Conclusions: Data linkage provided enhancements to the risk models underpinning national clinical audits in the form of additional predictors and novel outcomes measures. The new models developed in this report may assist in providing objective estimates of potential outcomes to patients and their families. Future work: (1) Develop and test care pathways for recovery following critical illness targeted at those with the greatest need; (2) explore other relevant data sources for longer-term outcomes; (3) widen data linkage for resource use and costs to primary care, outpatient and emergency department data. Study registration: This study is registered as NCT02454257. Funding details: This project was funded by the National Institute for Health and Care Research (NIHR) Health and Social Care Delivery Research programme and will be published in full in Health and Social Care Delivery Research; Vol. 10, No. 39. See the NIHR Journals Library website for further project information.

It is critical that the development of new technologies to solve environmental problems do not shift the burdens (impacts) to other mediums. Therefore, to ensure the optimum environmental design of water treatment and recovery systems, it is pertinent to apply life cycle assessment (LCA) at an early stage of development. Using LCA at an early developmental stage is known as prospective LCA and is particularly challenging due to the low data availability or quality. The aim of this study is to highlight the opportunities and challenges of using prospective LCA in the development of water treatment and recovery technologies. To do this we utilise two case studies from the EU Zero Brine project and apply LCA at two developmental stages. The treatment systems are specifically tailored for each case to treat the individual brine compositions and selectively recover its constituents. The first stage LCAs are based on laboratory experiments and engineering-based calculations, whilst the second stage assessments use improved input data from pilot plants and simulation. The paper compares the analyses of both stages, identifies key differences and discusses these disparities. In addition, it provides insights on the challenges of applying LCA for the design and development of wastewater treatment and recovery systems.

Coal-mine effluent treatment has the potential to both reduce the environmental impact of the effluent and provide economic opportunities by recovering valuable minerals and clean water. In this study, we modeled a novel treatment process, which includes nanofiltration (NF), two-step crystallization, reverse osmosis (RO), electrodialysis (ED), multi-effect distillation (MED), and a NaCl crystallizer, and performed a techno-economic analysis of its full-scale implementation, using a circular economy approach. We estimated the thermal and electrical energy consumption to be 745.5 kWhth/tonNaCl and 565.1 kWhel/tonNaCl (or 13.6 kWhth and 10.3 kWhel per m3 of feed effluent), respectively. The levelized cost of the NaCl salt that accounts for the revenue from the plant's co-products (Mg(OH)2, CaSO4 and, pure water) was estimated to be 203 USD/tonNaCl. The economic viability of the treatment chain can be improved by using renewable electricity sources, reducing the total expenditure on NF and RO, and integrating alternate technologies into the treatment plant.

The European Union's Environmental Technology Verification (ETV) program aims to foster innovative environmental technologies to reach the market and reassure potential users. This paper presents an investigation of using ETV for three technologies, being developed within the EU Zero Brine research and innovation project. The technologies were designed to recover high quality water, salts and minerals from brine solutions. The technologies in focus are the forward feed MED evaporator, the Multi Feed – Plug Flow Reactor Crystalliser and Eutectic Freeze Crystallization. The study sought to understand the challenges of the ETV process, the readiness and eligibility of technologies, and possible preparations within the project lifetime. Challenges identified included: understanding what sufficient market readiness is, and achieving this within the duration of a project (also linked to funding allocation for the ETV process); and developing suitable performance claims, supported with sufficient levels of test data. A simple framework is presented to aid the integration of ETV into the development process. It promotes the use of life cycle assessment to understand the environmental added value of the technology and aid the development of performance claims.

Abstract Background An Informatics Consult has been proposed in which clinicians request novel evidence from large scale health data resources, tailored to the treatment of a specific patient. However, the availability of such consultations is lacking. We seek to provide an Informatics Consult for a situation where a treatment indication and contraindication coexist in the same patient, i.e., anti-coagulation use for stroke prevention in a patient with both atrial fibrillation (AF) and liver cirrhosis. Methods We examined four sources of evidence for the effect of warfarin on stroke risk or all-cause mortality from: (1) randomised controlled trials (RCTs), (2) meta-analysis of prior observational studies, (3) trial emulation (using population electronic health records (N = 3,854,710) and (4) genetic evidence (Mendelian randomisation). We developed prototype forms to request an Informatics Consult and return of results in electronic health record systems. Results We found 0 RCT reports and 0 trials recruiting for patients with AF and cirrhosis. We found broad concordance across the three new sources of evidence we generated. Meta-analysis of prior observational studies showed that warfarin use was associated with lower stroke risk (hazard ratio [HR] = 0.71, CI 0.39–1.29). In a target trial emulation, warfarin was associated with lower all-cause mortality (HR = 0.61, CI 0.49–0.76) and ischaemic stroke (HR = 0.27, CI 0.08–0.91). Mendelian randomisation served as a drug target validation where we found that lower levels of vitamin K1 (warfarin is a vitamin K1 antagonist) are associated with lower stroke risk. A pilot survey with an independent sample of 34 clinicians revealed that 85% of clinicians found information on prognosis useful and that 79% thought that they should have access to the Informatics Consult as a service within their healthcare systems. We identified candidate steps for automation to scale evidence generation and to accelerate the return of results. Conclusion We performed a proof-of-concept Informatics Consult for evidence generation, which may inform treatment decisions in situations where there is dearth of randomised trials. Patients are surprised to know that their clinicians are currently not able to learn in clinic from data on ‘patients like me’. We identify the key challenges in offering such an Informatics Consult as a service.

The association of liver biochemistry with clinical outcomes of severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2) infection is currently unclear, and the utility of longitudinally measured liver biochemistry as prognostic markers for mortality is unknown. We aimed to determine whether abnormal liver biochemistry, assessed at baseline and at repeat measures over time, was associated with death in hospitalized patients with COVID‐19 compared to those without COVID‐19, in a United Kingdom population. We extracted routinely collected clinical data from a large teaching hospital in the United Kingdom, matching 585 hospitalized patients who were SARS‐CoV‐2 real‐time reverse transcription‐polymerase chain reaction (RT‐PCR) positive to 1,165 hospitalized patients who were RT‐PCR negative for age, sex, ethnicity, and preexisting comorbidities. A total of 26.8% (157/585) of patients with COVID‐19 died compared to 11.9% (139/1,165) in the group without COVID‐19 (P

Objective Colorectal cancer is a common cause of death and morbidity. A significant amount of data are routinely collected during patient treatment, but they are not generally available for research. The National Institute for Health Research Health Informatics Collaborative in the UK is developing infrastructure to enable routinely collected data to be used for collaborative, cross-centre research. This paper presents an overview of the process for collating colorectal cancer data and explores the potential of using this data source.Methods Clinical data were collected from three pilot Trusts, standardised and collated. Not all data were collected in a readily extractable format for research. Natural language processing (NLP) was used to extract relevant information from pseudonymised imaging and histopathology reports. Combining data from many sources allowed reconstruction of longitudinal histories for each patient that could be presented graphically.Results Three pilot Trusts submitted data, covering 12 903 patients with a diagnosis of colorectal cancer since 2012, with NLP implemented for 4150 patients. Timelines showing individual patient longitudinal history can be grouped into common treatment patterns, visually presenting clusters and outliers for analysis. Difficulties and gaps in data sources have been identified and addressed.Discussion Algorithms for analysing routinely collected data from a wide range of sites and sources have been developed and refined to provide a rich data set that will be used to better understand the natural history, treatment variation and optimal management of colorectal cancer.Conclusion The data set has great potential to facilitate research into colorectal cancer.

Machine Learning for Health (ML4H) has demonstrated efficacy in computer imaging and other self-contained digital workflows, but has failed to substantially impact routine clinical care. This is no longer because of poor adoption of Electronic Health Records Systems (EHRS), but because ML4H needs an infrastructure for development, deployment and evaluation within the healthcare institution. In this paper, we propose a design pattern called a Clinical Deployment Environment (CDE). We sketch the five pillars of the CDE: (1) real world development supported by live data where ML4H teams can iteratively build and test at the bedside (2) an ML-Ops platform that brings the rigour and standards of continuous deployment to ML4H (3) design and supervision by those with expertise in AI safety (4) the methods of implementation science that enable the algorithmic insights to influence the behaviour of clinicians and patients and (5) continuous evaluation that uses randomisation to avoid bias but in an agile manner. The CDE is intended to answer the same requirements that bio-medicine articulated in establishing the translational medicine domain. It envisions a transition from “real-world” data to “real-world” development.

BackgroundPreoperative risk prediction is important for guiding clinical decision-making and resource allocation. Clinicians frequently rely solely on their own clinical judgement for risk prediction rather than objective measures. We aimed to compare the accuracy of freely available objective surgical risk tools with subjective clinical assessment in predicting 30-day mortality.Methods and findingsWe conducted a prospective observational study in 274 hospitals in the United Kingdom (UK), Australia, and New Zealand. For 1 week in 2017, prospective risk, surgical, and outcome data were collected on all adults aged 18 years and over undergoing surgery requiring at least a 1-night stay in hospital. Recruitment bias was avoided through an ethical waiver to patient consent; a mixture of rural, urban, district, and university hospitals participated. We compared subjective assessment with 3 previously published, open-access objective risk tools for predicting 30-day mortality: the Portsmouth-Physiology and Operative Severity Score for the enUmeration of Mortality (P-POSSUM), Surgical Risk Scale (SRS), and Surgical Outcome Risk Tool (SORT). We then developed a logistic regression model combining subjective assessment and the best objective tool and compared its performance to each constituent method alone. We included 22,631 patients in the study: 52.8% were female, median age was 62 years (interquartile range [IQR] 46 to 73 years), median postoperative length of stay was 3 days (IQR 1 to 6), and inpatient 30-day mortality was 1.4%. Clinicians used subjective assessment alone in 88.7% of cases. All methods overpredicted risk, but visual inspection of plots showed the SORT to have the best calibration. The SORT demonstrated the best discrimination of the objective tools (SORT Area Under Receiver Operating Characteristic curve [AUROC] = 0.90, 95% confidence interval [CI]: 0.88-0.92; P-POSSUM = 0.89, 95% CI 0.88-0.91; SRS = 0.85, 95% CI 0.82-0.87). Subjective assessment demonstrated good discrimination (AUROC = 0.89, 95% CI: 0.86-0.91) that was not different from the SORT (p = 0.309). Combining subjective assessment and the SORT improved discrimination (bootstrap optimism-corrected AUROC = 0.92, 95% CI: 0.90-0.94) and demonstrated continuous Net Reclassification Improvement (NRI = 0.13, 95% CI: 0.06-0.20, p < 0.001) compared with subjective assessment alone. Decision-curve analysis (DCA) confirmed the superiority of the SORT over other previously published models, and the SORT-clinical judgement model again performed best overall. Our study is limited by the low mortality rate, by the lack of blinding in the 'subjective' risk assessments, and because we only compared the performance of clinical risk scores as opposed to other prediction tools such as exercise testing or frailty assessment.ConclusionsIn this study, we observed that the combination of subjective assessment with a parsimonious risk model improved perioperative risk estimation. This may be of value in helping clinicians allocate finite resources such as critical care and to support patient involvement in clinical decision-making.

Deficiency of complement factor I is a rare immunodeficiency that typically presents with increased susceptibility to encapsulated bacterial infections. However, non-infectious presentations including rheumatological, dermatological and neurological disease are increasingly recognized and require a high-index of suspicion to reach a timely diagnosis. Herein, we present two contrasting cases of complement factor I deficiency: one presenting in childhood with invasive pneumococcal disease, diagnosed using conventional immunoassays and genetics and the second presenting in adolescence with recurrent sterile neuroinflammation, diagnosed via a genomic approach. Our report and review of the literature highlight the wide spectrum of clinical presentations associated with CFI deficiency and the power of genomic medicine to inform rare disease diagnoses.

BACKGROUND:Prognostication is an essential tool for risk adjustment and decision making in the intensive care unit (ICU). Research into prognostication in ICU has so far been limited to data from admission or the first 24 hours. Most ICU admissions last longer than this, decisions are made throughout an admission, and some admissions are explicitly intended as time-limited prognostic trials. Despite this, temporal changes in prognostic ability during ICU admission has received little attention to date. Current predictive models, in the form of prognostic clinical tools, are typically derived from linear models and do not explicitly handle incremental information from trends. Machine learning (ML) allows predictive models to be developed which use non-linear predictors and complex interactions between variables, thus allowing incorporation of trends in measured variables over time; this has made it possible to investigate prognosis throughout an admission. METHODS AND FINDINGS:This study uses ML to assess the predictability of ICU mortality as a function of time. Logistic regression against physiological data alone outperformed APACHE-II and demonstrated several important interactions including between lactate & noradrenaline dose, between lactate & MAP, and between age & MAP consistent with the current sepsis definitions. ML models consistently outperformed logistic regression with Deep Learning giving the best results. Predictive power was maximal on the second day and was further improved by incorporating trend data. Using a limited range of physiological and demographic variables, the best machine learning model on the first day showed an area under the receiver-operator characteristic curve (AUC) of 0.883 (σ = 0.008), compared to 0.846 (σ = 0.010) for a logistic regression from the same predictors and 0.836 (σ = 0.007) for a logistic regression based on the APACHE-II score. Adding information gathered on the second day of admission improved the maximum AUC to 0.895 (σ = 0.008). Beyond the second day, predictive ability declined. CONCLUSION:This has implications for decision making in intensive care and provides a justification for time-limited trials of ICU therapy; the assessment of prognosis over more than one day may be a valuable strategy as new information on the second day helps to differentiate outcomes. New ML models based on trend data beyond the first day could greatly improve upon current risk stratification tools.

OBJECTIVE: To compare the effectiveness of melarsoprol and eflornithine in treating late-stage Gambian trypanosomiasis in the Republic of the Congo. METHODS: We analysed the outcomes of death during treatment and relapse within 1 year of discharge for 288 patients treated with eflornithine, 311 patients treated with the standard melarsoprol regimen and 62 patients treated with a short-course (10-day) melarsoprol regimen between April 2001 and April 2005. FINDINGS: A total of 1.7% (5/288) of patients treated with eflornithine died compared with 4.8% (15/311) of those treated with standard melarsoprol and 6.5% (4/62) of those treated with short-course melarsoprol. Patients treated with eflornithine tended to be younger and were more likely to have trypanosomes or higher white blood cell counts in their cerebrospinal fluid. The cumulated incidence of relapse among patients who attended at least one follow-up visit 1 year after discharge was 8.1% (11/136) for those treated with eflornithine, 14% (36/258) for those treated with standard melarsoprol and 15.5% (9/58) for those treated with shortcourse melarsoprol. In a multivariate analysis, when compared with eflornithine, standard melarsoprol was found to be a risk factor for both death (odds ratio (OR) = 2.87; 95% confidence interval (CI) = 1.03-8.00) and relapse (hazard ratio (HR) = 2.47; 95% CI = 1.22-5.03); when compared with eflornithine, short-course melarsoprol was also found to be a risk factor for death (OR = 3.90; 95% CI = 1.02-14.98) and relapse (HR = 6.65; 95% CI = 2.61-16.94). CONCLUSION: The effectiveness of melarsoprol treatment appears to have diminished. Eflornithine seems to be a better first-line therapy for treating late-stage Gambian trypanosomiasis in the Republic of the Congo.

OBJECTIVE: In 2002-03, the Republic of the Congo increased the threshold separating stage 1 and 2 cases of human African trypanosomiasis (HAT) from a cerebrospinal fluid (CSF) white cell count of 5 cells/mm³ to 10 cells/mm³. We aimed to assess whether the increased threshold of 10 cells/mm³ is a safe indicator of stage 2 disease. METHODS: We assessed patients treated for stage 1 HAT caused by Trypanosoma brucei gambiense in the Republic of the Congo between April 2001 and April 2005. Patients with 0-10 cells/mm³ in CSF were classed as stage 1 and treated with pentamidine. Patients with CSF of > 10 cells/mm³ were classed as stage 2 and treated with either melarsoprol or eflornithine. We did a retrospective analysis of all patients treated after the September 2002 increase in threshold for classification of HAT disease stage 2, and who were eligible for at least 1 year of follow-up. Primary outcome was survival without death or relapse within 1 year of discharge. Risk factors for treatment failure, in particular CSF white cell count on diagnosis, were assessed. FINDINGS: Between September 2002 to April 2004, 692 patients eligible for our analysis were treated with pentamidine. All were discharged alive. Relapse rate was 5% (n = 33). The only identified risk factor for relapse was a CSF white cell count of 6-10 cells/mm³ rather than 0-5 cells/mm³ (adjusted hazard ratio 3.27 (95% confidence interval, 1.52-7.01); P = 0.002). CONCLUSION: A threshold of 5 white cells/mm³ in CSF is safer than 10 cells/mm³ to determine stage 2 HAT and reduce risk of relapse.

The Kwinana Industrial Area of Western Australia has emerged as a world leading example of industrial symbiosis. This involves businesses in close proximity exchanging by-product material, water and energy. Utilisation of a previously discarded resource as an alternative input to another company can help improve both business and sustainability performance of the participating companies. For example, the exchange can reduce disposal costs and provide a cheaper input for the receiving company. The environmental benefits can include reduced collective resource consumption and waste generation, whilst the social benefits may include new employment opportunities and reduction of emission (e.g. water or traffic) to the local community. This article presents the integrated research programme undertaken at the Centre of Excellence in Cleaner Production, Curtin University of Technology which seeks to enhance the uptake of industrial symbiosis in Australian heavy industrial areas. The case of Kwinana is discussed with illustrative case studies of industrial symbiosis exchanges. International interest in the creation of industrial symbiosis continues to grow and the article concludes with a discussion on the emerging role of measurement and control technolo