Machine Learning for the Early Detection of Acute Episodes in Intensive Care Units

In Intensive Care Units (ICUs), mere seconds might define whether a patient lives or dies. Predictive models capable of detecting acute events in advance may allow for anticipated interventions, which could mitigate the consequences of those events and promote a greater number of lives saved. Several predictive models developed for this purpose have failed to meet the high requirements of ICUs. This might be due to the complexity of anomaly prediction tasks, and the inefficient utilization of ICU data. Moreover, some essential intensive care demands, such as continuous monitoring, are often not considered when developing these solutions, making them unfit to real contexts. This work approaches two topics within the mentioned problem: the relevance of ICU data used to predict acute episodes and the benefits of applying Layered Learning (LL) techniques to counter the complexity of these tasks. The first topic was undertaken through a study on the relevance of information retrieved from physiological signals and clinical data for the early detection of Acute Hypotensive Episodes (AHE) in ICUs. Then, the potentialities of LL were accessed through an in-depth analysis of the applicability of a recently proposed approach on the same topic. Furthermore, different optimization strategies enabled by LL configurations were proposed, including a new approach aimed at false alarm reduction. The results regarding data relevance might contribute to a shift in paradigm in terms of information retrieved for AHE prediction. It was found that most of the information commonly used in the literature might be wrongly perceived as valuable, since only three features related to blood pressure measures presented actual distinctive traits. On another note, the different LL-based strategies developed confirm the versatile possibilities offered by this paradigm. Although these methodologies did not promote significant performance improvements in this specific context, they can be further explored and adapted to other domains.Em Unidades de Cuidados Intensivos (UCIs), meros segundos podem ser o fator determinante entre a vida e a morte de um paciente. Modelos preditivos para a previsão de eventos adversos podem promover intervenções antecipadas, com vista à mitigação das consequências destes eventos, e traduzir-se num maior número de vidas salvas. Múltiplos modelos desenvolvidos para este propósito não corresponderam às exigências das UCIs. Isto pode dever-se à complexidade de tarefas de previsão de anomalias e à ineficiência no uso da informação gerada em UCIs. Além disto, algumas necessidades inerentes à provisão de cuidados intensivos, tais como a monitorização contínua, são muitas vezes ignoradas no desenvolvimento destas soluções, tornando-as desadequadas para contextos reais. Este projeto aborda dois tópicos dentro da problemática introduzida, nomeadamente a relevância da informação usada para prever episódios agudos, e os benefícios de técnicas de Aprendizagem em Camadas (AC) para contrariar a complexidade destas tarefas. Numa primeira fase, foi conduzido um estudo sobre o impacto de diversos sinais fisiológicos e dados clínicos no contexto da previsão de episódios agudos de hipotensão. As potencialidades do paradigma de AC foram avaliadas através da análise de uma abordagem proposta recentemente para o mesmo caso de estudo. Nesta segunda fase, diversas estratégias de otimização compatíveis com configurações em camadas foram desenvolvidas, incluindo um modelo para reduzir falsos alarmes. Os resultados relativos à relevância da informação podem contribuir para uma mudança de paradigma em termos da informação usada para treinar estes modelos. A maior parte da informação poderá estar a ser erroneamente considerada como importante, uma vez que apenas três variáveis, deduzidas dos valores de pressão arterial, foram identificadas como realmente impactantes. Por outro lado, as diferentes estratégias baseadas em AC confirmaram a versatilidade oferecida por este paradigma. Apesar de não terem promovido melhorias significativas neste contexto, estes métodos podem ser adaptados a outros domínios

Clinical and haemodynamic studies in portal hypertension

Over the last 20 years, there have been significant advances in the management of portal hypertension, with the introduction of drug therapy and the transjugular intrahepatic portosystemic stent-shunt (TIPSS). This development continues at a strong pace as our understanding of the pathogenesis of portal hypertension deepens. There are 2 aims of this thesis:1. To study the haemodynamic effects of two novel vasoactive agents on the portal and systemic circulations.a. Carvedilol, a vasodilating non-cardioselective beta-blocker with op antagonism. The acute and chronic haemodynamic effects of this agent will be studied, with particular attention paid to patient tolerability.b. Losartan, an angiotensin II receptor antagonist. The chronic effects of this agent will be studied in patients with well compensated cirrhosis.These laboratory based studies will assist in determining the suitability of these agents for use in controlled clinical trials on patients at risk of variceal bleeding.2. TIPSS has been used extensively in the management of portal hypertension, particularly variceal bleeding. Two studies will be presented in this thesis aimed at answering the following questions.a. Is TIPSS effective for the management of gastric variceal bleeding? Gastric variceal bleeding is less common than oesophageal variceal bleeding, hence there are relatively few studies investigating the effect of TIPSS on bleeding gastric varices. This study will also compare gastric variceal bleeding with oesophageal variceal bleeding, and aim to correlate clinical outcomes with haemodynamic data.b. Is it necessary to continue portographic TIPSS surveillance indefinitely if variceal band ligation is combined with TIPSS for the prevention of oesophageal variceal rebleeding? This is the hypothesis for a randomised controlled trial comparing TIPSS alone with TIPSS plus variceal band ligation. This study will address 2 drawbacks of TIPSS, namely the need for long-term portographic to ensure TIPSS patency and hepatic encephalopathy

Perioperative risk factors and outcomes

Perioperative complications is an increasing issue worldwide, as surgical volume continues to grow. Myocardial and kidney injury, and myocardial infarction (MI), are known complications in non-cardiac surgery. Hemodynamic instability during anaesthesia and surgery, the association with perioperative complications, and optimal blood pressure threshold in the perioperative period, have been topics of increasing interest since this thesis idea was formed. The thesis aim is to increase our knowledge of perioperative organ injury and to understand its aetiology: to evaluate the relation between preoperative risk factors – comorbid burden – and intraoperative risk factors, with a special focus on intraoperative hemodynamic variability. All studies are observational by design and epidemiologically approached. Regional and national registers, and medical records, are used in the data collection. Study I is a descriptive, registry-based, cohort study of more than 400 000 operated adult patientes in 22 Swedish hospitals between 2007 and 2014. Study II and III are cohort studies enrolling adult patients undergoing major non-cardiac surgery att the Karolinska University Hospital, 2012 to 2013 and 2015 to 2016. Study IV use a case-control study design, nested within the cohort collected in study I. In summary, this thesis illuminates how comorbid patients, undergoing major non-cardiac surgical procedures, are at increased risk of perioperative cardiac and kidney morbidity. Development of myocardial or kidney injury, or clinically significant MI in the perioperative period is associated with short- and longterm mortality. This elderly, high-risk surgical population should be targeted to improve perioperative outcomes. Intraoperative hypotension is associated with myocardial and kidney injury and is a major contributor to clinically significant perioperative MI. The high absolute risk of MI development associated with intraoperative hypotension, among a growing population of patients with a high risk-burden, suggests that increased vigilance of blood pressure control in these patients is beneficia

Prediction of short-term health outcomes in preterm neonates from heart-rate variability and blood pressure using boosted decision trees

Background and Objective: Efficient management of low blood pressure (BP) in preterm neonates remains challenging with considerable variability in clinical practice. There is currently no clear consensus on what constitutes a limit for low BP that is a risk to the preterm brain. It is argued that a personalised approach rather than a population based threshold is more appropriate. This work aims to assist healthcare professionals in assessing preterm wellbeing during episodes of low BP in order to decide when and whether hypotension treatment should be initiated. In particular, the study investigates the relationship between heart rate variability (HRV) and BP in preterm infants and its relevance to a short-term health outcome. Methods: The study is performed on a large clinically collected dataset of 831 h from 23 preterm infants of less than 32 weeks gestational age. The statistical predictive power of common HRV features is first assessed with respect to the outcome. A decision support system, based on boosted decision trees (XGboost), was developed to continuously estimate the probability of neonatal morbidity based on the feature vector of HRV characteristics and the mean arterial blood pressure. Results: It is shown that the predictive power of the extracted features improves when observed during episodes of hypotension. A single best HRV feature achieves an AUC of 0.87. Combining multiple HRV features extracted during hypotensive episodes with the classifier achieves an AUC of 0.97, using a leave-one-patient-out performance assessment. Finally it is shown that good performance can even be achieved using continuous HRV recordings, rather than only focusing on hypotensive events – this had the benefit of not requiring invasive BP monitoring. Conclusions: The work presents a promising step towards the use of multimodal data in providing objective decision support for the prediction of short-term outcome in preterm infants with hypotensive episodes

Acute lung injury in paediatric intensive care: course and outcome

Introduction: Acute lung injury (ALI) and acute respiratory distress syndrome (ARDS) carry a high morbidity and mortality (10-90%). ALI is characterised by non-cardiogenic pulmonary oedema and refractory hypoxaemia of multifactorial aetiology [1]. There is limited data about outcome particularly in children. Methods This retrospective cohort study of 85 randomly selected patients with respiratory failure recruited from a prospectively collected database represents 7.1% of 1187 admissions. They include those treated with High Frequency Oscillation Ventilation (HFOV). The patients were admitted between 1 November 1998 and 31 October 2000. Results: Of the 85, 49 developed acute lung injury and 47 had ARDS. There were 26 males and 23 females with a median age and weight of 7.7 months (range 1 day-12.8 years) and 8 kg (range 0.8-40 kg). There were 7 deaths giving a crude mortality of 14.3%, all of which fulfilled the Consensus I [1] criteria for ARDS. Pulmonary occlusion pressures were not routinely measured. The A-a gradient and PaO2/FiO2 ratio (median + [95% CI]) were 37.46 [31.82-43.1] kPa and 19.12 [15.26-22.98] kPa respectively. The non-survivors had a significantly lower PaO2/FiO2 ratio (13 [6.07-19.93] kPa) compared to survivors (23.85 [19.57-28.13] kPa) (P = 0.03) and had a higher A-a gradient (51.05 [35.68-66.42] kPa) compared to survivors (36.07 [30.2-41.94]) kPa though not significant (P = 0.06). Twenty-nine patients (59.2%) were oscillated (Sensormedics 3100A) including all 7 non-survivors. There was no difference in ventilation requirements for CMV prior to oscillation. Seventeen of the 49 (34.7%) were treated with Nitric Oxide including 5 out of 7 non-survivors (71.4%). The median (95% CI) number of failed organs was 3 (1.96-4.04) for non-survivors compared to 1 (0.62-1.62) for survivors (P = 0.03). There were 27 patients with isolated respiratory failure all of whom survived. Six (85.7%) of the non-survivors also required cardiovascular support.Conclusion: A crude mortality of 14.3% compares favourably to published data. The A-a gradient and PaO2/FiO2 ratio may be of help in morbidity scoring in paediatric ARDS. Use of Nitric Oxide and HFOV is associated with increased mortality, which probably relates to the severity of disease. Multiple organ failure particularly respiratory and cardiac disease is associated with increased mortality. ARDS with isolated respiratory failure carries a good prognosis in children

Severe sepsis: variation in resource and therapeutic modality use among academic centers

BACKGROUND: Treatment of severe sepsis is expensive, often encompassing a number of discretionary modalities. The objective of the present study was to assess intercenter variation in resource and therapeutic modality use in patients with severe sepsis. METHODS: We conducted a prospective cohort study of 1028 adult admissions with severe sepsis from a stratified random sample of patients admitted to eight academic tertiary care centers. The main outcome measures were length of stay (LOS; total LOS and LOS after onset of severe sepsis) and total hospital charges. RESULTS: The adjusted mean total hospital charges varied from $69 429 to US$237 898 across centers, whereas the adjusted LOS after onset varied from 15.9 days to 24.2 days per admission. Treatments used frequently after the first onset of sepsis among patients with severe sepsis were pulmonary artery catheters (19.4%), ventilator support (21.8%), pressor support (45.8%) and albumin infusion (14.4%). Pulmonary artery catheter use, ventilator support and albumin infusion had moderate variation profiles, varying 3.2-fold to 4.9-fold, whereas the rate of pressor support varied only 1.92-fold across centers. Even after adjusting for age, sex, Charlson comorbidity score, discharge diagnosis-relative group weight, organ dysfunction and service at onset, the odds for using these therapeutic modalities still varied significantly across centers. Failure to start antibiotics within 24 hours was strongly correlated with a higher probability of 28-day mortality (r(2 )= 0.72). CONCLUSION: These data demonstrate moderate but significant variation in resource use and use of technologies in treatment of severe sepsis among academic centers. Delay in antibiotic therapy was associated with worse outcome at the center level

Incidence, risk factors and prognosis of changes in serum creatinine early after aortic abdominal surgery

Objective: To determine the incidence, risk factors, and prognostic implications of serum creatinine changes following major vascular surgery. Design: Observational study. Settings: University hospital. Patients: Cohort of 599 consecutive patients undergoing elective abdominal aortic surgery. Interventions: Review of prospectively collected data from 1993 to 2004. Measurements and results: The receiver-operator characteristic (ROC) curve analysis was used to detect the best threshold for postoperative elevation in serum creatinine (Δ Creat) in relation to major complications. Acut-off value of +0.5 mg/dl was selected to define renal dysfunction (RD0.5 group, n = 91; no RD0.5, n = 508) that was associated with higher mortality (7.7% in RD0.5 group vs 1.4% in no RD0.5 group, P  40 min; OR, 3.8, 95% CI, 1.9-7.2), blood transfusion (> 5 units; OR, 1.9, 95% CI 1.2-6.1), and rhabdomyolysis (OR, 3.6, 95% CI 1.7-7.9). Conclusions: Postoperative RD0.5 (Δ Creat  > 0.5 mg/dl) occurs in 15% of vascular patients and carries abad prognosis. Preoperative renal insufficiency and factors related to the complexity of surgery are the main predictors of renal dysfunctio

The use of knowledge discovery databases in the identification of patients with colorectal cancer

Colorectal cancer is one of the most common forms of malignancy with 35,000 new patients diagnosed annually within the UK. Survival figures show that outcomes are less favourable within the UK when compared with the USA and Europe with 1 in 4 patients having incurable disease at presentation as of data from 2000.Epidemiologists have demonstrated that the incidence of colorectal cancer is highest on the industrialised western world with numerous contributory factors. These range from a genetic component to concurrent medical conditions and personal lifestyle. In addition, data also demonstrates that environmental changes play a significant role with immigrants rapidly reaching the incidence rates of the host country.Detection of colorectal cancer remains an important and evolving aspect of healthcare with the aim of improving outcomes by earlier diagnosis. This process was initially revolutionised within the UK in 2002 with the ACPGBI 2 week wait guidelines to facilitate referrals form primary care and has subsequently seen other schemes such as bowel cancer screening introduced to augment earlier detection rates. Whereas the national screening programme is dependent on FOBT the standard referral practice is dependent upon a number of trigger symptoms that qualify for an urgent referral to a specialist for further investigations. This process only identifies 25-30% of those with colorectal cancer and remains a labour intensive process with only 10% of those seen in the 2 week wait clinics having colorectal cancer.This thesis hypothesises whether using a patient symptom questionnaire in conjunction with knowledge discovery techniques such as data mining and artificial neural networks could identify patients at risk of colorectal cancer and therefore warrant urgent further assessment. Artificial neural networks and data mining methods are used widely in industry to detect consumer patterns by an inbuilt ability to learn from previous examples within a dataset and model often complex, non-linear patterns. Within medicine these methods have been utilised in a host of diagnostic techniques from myocardial infarcts to its use in the Papnet cervical smear programme for cervical cancer detection.A linkert based questionnaire of those attending the 2 week wait fast track colorectal clinic was used to produce a ‘symptoms’ database. This was then correlated with individual patient diagnoses upon completion of their clinical assessment. A total of 777 patients were included in the study and their diagnosis categorised into a dichotomous variable to create a selection of datasets for analysis. These data sets were then taken by the author and used to create a total of four primary databases based on all questions, 2 week wait trigger symptoms, Best knowledge questions and symptoms identified in Univariate analysis as significant. Each of these databases were entered into an artificial neural network programme, altering the number of hidden units and layers to obtain a selection of outcome models that could be further tested based on a selection of set dichotomous outcomes. Outcome models were compared for sensitivity, specificity and risk. Further experiments were carried out with data mining techniques and the WEKA package to identify the most accurate model. Both would then be compared with the accuracy of a colorectal specialist and GP.Analysis of the data identified that 24% of those referred on the 2 week wait referral pathway failed to meet referral criteria as set out by the ACPGBI. The incidence of those with colorectal cancer was 9.5% (74) which is in keeping with other studies and the main symptoms were rectal bleeding, change in bowel habit and abdominal pain. The optimal knowledge discovery database model was a back propagation ANN using all variables for outcomes cancer/not cancer with sensitivity of 0.9, specificity of 0.97 and LR 35.8. Artificial neural networks remained the more accurate modelling method for all the dichotomous outcomes.The comparison of GP’s and colorectal specialists at predicting outcome demonstrated that the colorectal specialists were the more accurate predictors of cancer/not cancer with sensitivity 0.27 and specificity 0.97, (95% CI 0.6-0.97, PPV 0.75, NPV 0.83) and LR 10.6. When compared to the KDD models for predicting the same outcome, once again the ANN models were more accurate with the optimal model having sensitivity 0.63, specificity 0.98 (95% CI 0.58-1, PPV 0.71, NPV 0.96) and LR 28.7.The results demonstrate that diagnosis colorectal cancer remains a challenging process, both for clinicians and also for computation models. KDD models have been shown to be consistently more accurate in the prediction of those with colorectal cancer than clinicians alone when used solely in conjunction with a questionnaire. It would be ill conceived to suggest that KDD models could be used as a replacement to clinician- patient interaction but they may aid in the acceleration of some patients for further investigations or ‘straight to test’ if used on those referred as routine patients