Introdução: Triagem para admissão em unidades de terapia intensiva (UTIs) é realizada rotineiramente e é comumente baseada somente no julgamento clínico, o que pode mascarar vieses e preconceitos. Neste estudo, foram avaliadas a reprodutibilidade e validade de um algoritmo de apoio a decisões de triagem em UTI. Também foi avaliado o efeito da implementação de um instrumento de auxílio à tomada de decisão para a priorização de vagas de UTI nas decisões de admissão em UTI. Foi avaliada, ainda, a acurácia da predição prognóstica dos médicos na população de pacientes em deterioração clínica aguda. Métodos: Para o primeiro objetivo do estudo, um algoritmo computadorizado para auxiliar as decisões de priorização de vagas em UTI foi desenvolvido para classificar pacientes nas categorias do sistema de priorização da \"Society of Critical Care Medicine (SCCM)\". Nove médicos experientes (experts) avaliaram quarenta vinhetas clínicas baseadas em pacientes reais. A referência foi definida como as prioridades classificadas por dois investigadores com acesso ao prontuário completo dos pacientes. As concordâncias entre as prioridades do algoritmo com as prioridades da referência e com as prioridades dos experts foram avaliadas. As correlações entre a prioridade do algoritmo e o julgamento clínico de adequação da admissão na UTI em contexto com e sem escassez de vagas também foram avaliadas. A validade foi ainda avaliada através da aplicação do algoritmo, retrospectivamente em uma coorte de 603 pacientes com solicitação de vagas de UTI, para correlação com desfechos clínicos. Para o segundo objetivo do estudo, um estudo prospectivo, quaseexperimental foi conduzido, antes (maio/2014 a novembro/2014, fase 1) e após (novembro/2014 a maio/2015, fase 2) a implementação de um instrumento de auxílio à tomada de decisão, que foi baseado no algoritmo descrito acima. Foi avaliado o impacto da implementação do instrumento de auxílio à tomada de decisão na ocorrência de admissões potencialmente inapropriadas na UTI em uma coorte de pacientes com solicitações urgentes de vaga de UTI. O desfecho primário foi a proporção de solicitações de vaga potencialmente inapropriadas que foram admitidas na UTI em até 48 horas após a solicitação. Solicitações de vaga potencialmente inapropriadas foram definidas como pacientes prioridade 4B, conforme diretrizes da SCCM de 1999, ou prioridade 5, conforme diretrizes da SCCM de 2016. Foram realizadas análises multivariadas com teste de interação entre fase e prioridades para avaliação dos efeitos diferenciados em cada estrato de prioridade. Para o terceiro objetivo do estudo, a predição prognóstica realizada pelo médico solicitante foi registrada no momento da solicitação de vaga de UTI. Resultados: No primeiro objetivo do estudo, a concordância entre as prioridades do algoritmo e as prioridades da referência foi substancial, com uma mediana de kappa de 0,72 (IQR 0,52-0,77). As prioridades do algoritmo evidenciaram uma maior reprodutibilidade entre os pares [kappa = 0,61 (IC95% 0,57-0,65) e mediana de percentagem de concordância = 0,64 (IQR 0,59-0,70)], quando comparada à reprodutibilidade entre os pares das prioridades dos experts [kappa = 0,51 (IC95% 0,47-0,55) e mediana de percentagem de concordância = 0,49 (IQR 0,44-0,56)], p=0,001. As prioridades do algoritmo também foram associadas ao julgamento clínico de adequação da admissão na UTI (vinhetas com prioridades 1, 2, 3 e 4 seriam admitidas no último leito de UTI em 83,7%, 61,2%, 45,2% e 16,8% dos cenários, respectivamente, p < 0,001) e com desfechos clínicos reais na coorte retrospectiva, como admissão na UTI, consultas com equipe de cuidados paliativos e mortalidade hospitalar. No segundo objetivo do estudo, 2374 solicitações urgentes de vaga de UTI foram avaliadas, das quais 1184 (53,8%) pacientes foram admitidos na UTI. A implementação do instrumento de auxílio à tomada de decisão foi associada com uma redução de admissões potencialmente inapropriadas na UTI, tanto utilizando a classificação de 1999 [adjOR (IC95%) = 0,36 (0,13-0,97), p = 0,043], quanto utilizando a classificação de 2016 [adjOR (IC95%) = 0,35 (0,13-0,96, p = 0,041)]. Não houve diferença em mortalidade entre as fases 1 e 2 do estudo. No terceiro objetivo do estudo, a predição prognóstica do médico solicitante foi associada com mortalidade. Ocorreram 593 (34,4%), 215 (66,4%) e 51 (94,4%) óbitos nos grupos com prognóstico de sobrevivência sem sequelas graves, sobrevivência com sequelas graves e nãosobrevivência, respectivamente (p < 0,001). Sensibilidade foi 31%, especificidade foi 91% e a área sob a curva ROC foi de 0,61 para predição de mortalidade hospitalar. Após análise multivariada, a gravidade da doença aguda, funcionalidade prévia e admissão na UTI foram associadas com uma maior chance de erro prognóstico, enquanto que uma predição de pior prognóstico foi associada a uma menor chance de erro prognóstico. O grau de expertise do médico solicitante não teve efeito na predição prognóstica. Discussão/Conclusão: Neste estudo, um algoritmo de apoio a decisões de triagem em UTI demonstrou boa reprodutibilidade e validade. Além disso, a implementação de um instrumento de auxílio à tomada de decisões para priorização de vagas de UTI foi associada a uma redução de admissões potencialmente inapropriadas na UTI. Também foi encontrado que a predição prognóstica dos médicos solicitantes foi associada a mortalidade hospitalar, porém a acurácia foi pobre, principalmente devido a uma baixa sensibilidade para detectar risco de morte / Introduction: Intensive care unit (ICU) admission triage is performed routinely and is often based solely on clinical judgment, which could mask biases. In this study, we sought to evaluate the reliability and validity of an algorithm to aid ICU triage decisions. We also aimed to evaluate the effect of implementing a decision-aid tool for ICU triage on ICU admission decisions. We also evaluated the accuracy of physician\'s prediction of hospital mortality in in acutely deteriorating patients. Methods: For the first objective of the study, a computerized algorithm to aid ICU triage decisions was developed to classify patients into the Society of Critical Care Medicine\'s prioritization system. Nine senior physicians evaluated forty clinical vignettes based on real patients. Reference standard was defined as the priorities ascribed by two investigators with full access to patient\'s records. Agreement of algorithm-based priorities with the reference standard and with intuitive priorities provided by the physicians were evaluated. Correlations between algorithm prioritization and physician\'s judgment of appropriateness of ICU admission in scarcity and non-scarcity settings were also evaluated. Validity was further assessed by retrospectively applying this algorithm to 603 patients with requests for ICU admission for association with clinical outcomes. For the second objective of the study, a prospective, quasi-experimental study was conducted, before (May 2014 to November 2014, phase 1) and after (November 2014 to May 2015, phase 2) the implementation of a decision-aid tool for ICU admission triage, which was based on the aforementioned algorithm. We assessed the impact of the implementation of the decision-aid tool in potentially inappropriate ICU admissions in a cohort of patients referred for urgent ICU admission. Primary outcome was the proportion of potentially inappropriate ICU referrals that were admitted to the ICU in 48 hours following referral. Potentially inappropriate ICU referrals were defined as priority 4B patients, as described by the 1999 Society of Critical Care Medicine (SCCM) guidelines and as priority 5 patients, as described by the 2016 SCCM guidelines. We conducted multivariate analyses and evaluated the interaction between phase and triage priorities to assess for differential effects in each priority strata. For the third objective of the study, physicians\' prognosis and other variables were recorded at the moment of ICU referral. Results: On the first objective of the study, agreement between algorithm-based priorities and the reference standard was substantial, with a median kappa of 0.72 (IQR 0.52-0.77). Algorithm-based priorities demonstrated higher interrater reliability [overall kappa of 0.61 (95%CI 0.57-0.65) and median percent agreement of 0.64 (IQR 0.59-0.70)] than physician\'s intuitive prioritization [overall kappa of 0.51 (95%CI 0.47-0.55) and median percent agreement of 0.49 (IQR 0.44-0.56)], p=0.001. Algorithm-based priorities were also associated with physicians\' judgment of appropriateness of ICU admission (priorities 1, 2, 3 and 4 vignettes would be admitted to the last ICU bed in 83.7%, 61.2%, 45.2% and 16.8% of the scenarios, respectively, p < 0.001) and with actual ICU admission, palliative care consultation and hospital mortality in the retrospective cohort. On the second objective of the study, of 2374 urgent ICU referrals, 1184 (53.8%) patients were admitted to the ICU. Implementation of the decision-aid tool was associated with a reduction of potentially inappropriate ICU admissions using the 1999 [adjOR (95% CI) = 0.36 (0.13-0.97), p = 0.043] or 2016 [adjOR (95%CI) = 0.35 (0.13-0.96, p = 0.041)] definitions. There was no difference on mortality between phases 1 and 2. On the third objective of the study, physician\'s prognosis was associated to hospital mortality. There were 593 (34.4%), 215 (66.4%) and 51 (94.4%) deaths in the groups ascribed a prognosis of survival without disabilities, survival with severe disabilities or no survival, respectively (p < 0.001). Sensitivity was 31%, specificity was 91% and the area under the ROC curve was 0.61 for prediction of mortality. After multivariable analysis, severity of illness, performance status and ICU admission were associated to an increased likelihood of incorrect classification, while worse predicted prognosis was associated to a lower chance of incorrect classification. Physician\'s level of expertise had no effect on predictive ability. Discussion/Conclusion: In this study, a ICU admission triage algorithm demonstrated good reliability and validity. Moreover, the implementation of a decision-aid tool for ICU triage was associated with a reduction of potentially inappropriate ICU admissions. It was also found that physician\'s prediction was associated to hospital mortality, but overall accuracy was poor, mainly due to low sensitivity to detect mortality risk

Introdução: Triagem para admissão em unidades de terapia intensiva (UTIs) é realizada rotineiramente e é comumente baseada somente no julgamento clínico, o que pode mascarar vieses e preconceitos. Neste estudo, foram avaliadas a reprodutibilidade e validade de um algoritmo de apoio a decisões de triagem em UTI. Também foi avaliado o efeito da implementação de um instrumento de auxílio à tomada de decisão para a priorização de vagas de UTI nas decisões de admissão em UTI. Foi avaliada, ainda, a acurácia da predição prognóstica dos médicos na população de pacientes em deterioração clínica aguda. Métodos: Para o primeiro objetivo do estudo, um algoritmo computadorizado para auxiliar as decisões de priorização de vagas em UTI foi desenvolvido para classificar pacientes nas categorias do sistema de priorização da \"Society of Critical Care Medicine (SCCM)\". Nove médicos experientes (experts) avaliaram quarenta vinhetas clínicas baseadas em pacientes reais. A referência foi definida como as prioridades classificadas por dois investigadores com acesso ao prontuário completo dos pacientes. As concordâncias entre as prioridades do algoritmo com as prioridades da referência e com as prioridades dos experts foram avaliadas. As correlações entre a prioridade do algoritmo e o julgamento clínico de adequação da admissão na UTI em contexto com e sem escassez de vagas também foram avaliadas. A validade foi ainda avaliada através da aplicação do algoritmo, retrospectivamente em uma coorte de 603 pacientes com solicitação de vagas de UTI, para correlação com desfechos clínicos. Para o segundo objetivo do estudo, um estudo prospectivo, quaseexperimental foi conduzido, antes (maio/2014 a novembro/2014, fase 1) e após (novembro/2014 a maio/2015, fase 2) a implementação de um instrumento de auxílio à tomada de decisão, que foi baseado no algoritmo descrito acima. Foi avaliado o impacto da implementação do instrumento de auxílio à tomada de decisão na ocorrência de admissões potencialmente inapropriadas na UTI em uma coorte de pacientes com solicitações urgentes de vaga de UTI. O desfecho primário foi a proporção de solicitações de vaga potencialmente inapropriadas que foram admitidas na UTI em até 48 horas após a solicitação. Solicitações de vaga potencialmente inapropriadas foram definidas como pacientes prioridade 4B, conforme diretrizes da SCCM de 1999, ou prioridade 5, conforme diretrizes da SCCM de 2016. Foram realizadas análises multivariadas com teste de interação entre fase e prioridades para avaliação dos efeitos diferenciados em cada estrato de prioridade. Para o terceiro objetivo do estudo, a predição prognóstica realizada pelo médico solicitante foi registrada no momento da solicitação de vaga de UTI. Resultados: No primeiro objetivo do estudo, a concordância entre as prioridades do algoritmo e as prioridades da referência foi substancial, com uma mediana de kappa de 0,72 (IQR 0,52-0,77). As prioridades do algoritmo evidenciaram uma maior reprodutibilidade entre os pares [kappa = 0,61 (IC95% 0,57-0,65) e mediana de percentagem de concordância = 0,64 (IQR 0,59-0,70)], quando comparada à reprodutibilidade entre os pares das prioridades dos experts [kappa = 0,51 (IC95% 0,47-0,55) e mediana de percentagem de concordância = 0,49 (IQR 0,44-0,56)], p=0,001. As prioridades do algoritmo também foram associadas ao julgamento clínico de adequação da admissão na UTI (vinhetas com prioridades 1, 2, 3 e 4 seriam admitidas no último leito de UTI em 83,7%, 61,2%, 45,2% e 16,8% dos cenários, respectivamente, p < 0,001) e com desfechos clínicos reais na coorte retrospectiva, como admissão na UTI, consultas com equipe de cuidados paliativos e mortalidade hospitalar. No segundo objetivo do estudo, 2374 solicitações urgentes de vaga de UTI foram avaliadas, das quais 1184 (53,8%) pacientes foram admitidos na UTI. A implementação do instrumento de auxílio à tomada de decisão foi associada com uma redução de admissões potencialmente inapropriadas na UTI, tanto utilizando a classificação de 1999 [adjOR (IC95%) = 0,36 (0,13-0,97), p = 0,043], quanto utilizando a classificação de 2016 [adjOR (IC95%) = 0,35 (0,13-0,96, p = 0,041)]. Não houve diferença em mortalidade entre as fases 1 e 2 do estudo. No terceiro objetivo do estudo, a predição prognóstica do médico solicitante foi associada com mortalidade. Ocorreram 593 (34,4%), 215 (66,4%) e 51 (94,4%) óbitos nos grupos com prognóstico de sobrevivência sem sequelas graves, sobrevivência com sequelas graves e nãosobrevivência, respectivamente (p < 0,001). Sensibilidade foi 31%, especificidade foi 91% e a área sob a curva ROC foi de 0,61 para predição de mortalidade hospitalar. Após análise multivariada, a gravidade da doença aguda, funcionalidade prévia e admissão na UTI foram associadas com uma maior chance de erro prognóstico, enquanto que uma predição de pior prognóstico foi associada a uma menor chance de erro prognóstico. O grau de expertise do médico solicitante não teve efeito na predição prognóstica. Discussão/Conclusão: Neste estudo, um algoritmo de apoio a decisões de triagem em UTI demonstrou boa reprodutibilidade e validade. Além disso, a implementação de um instrumento de auxílio à tomada de decisões para priorização de vagas de UTI foi associada a uma redução de admissões potencialmente inapropriadas na UTI. Também foi encontrado que a predição prognóstica dos médicos solicitantes foi associada a mortalidade hospitalar, porém a acurácia foi pobre, principalmente devido a uma baixa sensibilidade para detectar risco de morte / Introduction: Intensive care unit (ICU) admission triage is performed routinely and is often based solely on clinical judgment, which could mask biases. In this study, we sought to evaluate the reliability and validity of an algorithm to aid ICU triage decisions. We also aimed to evaluate the effect of implementing a decision-aid tool for ICU triage on ICU admission decisions. We also evaluated the accuracy of physician\'s prediction of hospital mortality in in acutely deteriorating patients. Methods: For the first objective of the study, a computerized algorithm to aid ICU triage decisions was developed to classify patients into the Society of Critical Care Medicine\'s prioritization system. Nine senior physicians evaluated forty clinical vignettes based on real patients. Reference standard was defined as the priorities ascribed by two investigators with full access to patient\'s records. Agreement of algorithm-based priorities with the reference standard and with intuitive priorities provided by the physicians were evaluated. Correlations between algorithm prioritization and physician\'s judgment of appropriateness of ICU admission in scarcity and non-scarcity settings were also evaluated. Validity was further assessed by retrospectively applying this algorithm to 603 patients with requests for ICU admission for association with clinical outcomes. For the second objective of the study, a prospective, quasi-experimental study was conducted, before (May 2014 to November 2014, phase 1) and after (November 2014 to May 2015, phase 2) the implementation of a decision-aid tool for ICU admission triage, which was based on the aforementioned algorithm. We assessed the impact of the implementation of the decision-aid tool in potentially inappropriate ICU admissions in a cohort of patients referred for urgent ICU admission. Primary outcome was the proportion of potentially inappropriate ICU referrals that were admitted to the ICU in 48 hours following referral. Potentially inappropriate ICU referrals were defined as priority 4B patients, as described by the 1999 Society of Critical Care Medicine (SCCM) guidelines and as priority 5 patients, as described by the 2016 SCCM guidelines. We conducted multivariate analyses and evaluated the interaction between phase and triage priorities to assess for differential effects in each priority strata. For the third objective of the study, physicians\' prognosis and other variables were recorded at the moment of ICU referral. Results: On the first objective of the study, agreement between algorithm-based priorities and the reference standard was substantial, with a median kappa of 0.72 (IQR 0.52-0.77). Algorithm-based priorities demonstrated higher interrater reliability [overall kappa of 0.61 (95%CI 0.57-0.65) and median percent agreement of 0.64 (IQR 0.59-0.70)] than physician\'s intuitive prioritization [overall kappa of 0.51 (95%CI 0.47-0.55) and median percent agreement of 0.49 (IQR 0.44-0.56)], p=0.001. Algorithm-based priorities were also associated with physicians\' judgment of appropriateness of ICU admission (priorities 1, 2, 3 and 4 vignettes would be admitted to the last ICU bed in 83.7%, 61.2%, 45.2% and 16.8% of the scenarios, respectively, p < 0.001) and with actual ICU admission, palliative care consultation and hospital mortality in the retrospective cohort. On the second objective of the study, of 2374 urgent ICU referrals, 1184 (53.8%) patients were admitted to the ICU. Implementation of the decision-aid tool was associated with a reduction of potentially inappropriate ICU admissions using the 1999 [adjOR (95% CI) = 0.36 (0.13-0.97), p = 0.043] or 2016 [adjOR (95%CI) = 0.35 (0.13-0.96, p = 0.041)] definitions. There was no difference on mortality between phases 1 and 2. On the third objective of the study, physician\'s prognosis was associated to hospital mortality. There were 593 (34.4%), 215 (66.4%) and 51 (94.4%) deaths in the groups ascribed a prognosis of survival without disabilities, survival with severe disabilities or no survival, respectively (p < 0.001). Sensitivity was 31%, specificity was 91% and the area under the ROC curve was 0.61 for prediction of mortality. After multivariable analysis, severity of illness, performance status and ICU admission were associated to an increased likelihood of incorrect classification, while worse predicted prognosis was associated to a lower chance of incorrect classification. Physician\'s level of expertise had no effect on predictive ability. Discussion/Conclusion: In this study, a ICU admission triage algorithm demonstrated good reliability and validity. Moreover, the implementation of a decision-aid tool for ICU triage was associated with a reduction of potentially inappropriate ICU admissions. It was also found that physician\'s prediction was associated to hospital mortality, but overall accuracy was poor, mainly due to low sensitivity to detect mortality risk

Objectives: The objectives of this research were to describe the interactions (formal and informal) in which macrocognitive functions occur and their location on a pediatric intensive care unit (PICU); describe challenges and facilitators of macrocognition using three constructs of space syntax (openness, connectivity, and visibility); and analyze the health care built environment (HCBE) using those constructs to explicate influences on macrocognition. Background: In high reliability, complex industries, macrocognition is an approach to develop new knowledge among interprofessional team members. Although macrocognitive functions have been analyzed in multiple health care settings, the effect of the HCBE on those functions has not been directly studied. The theoretical framework, “Macrocognition in the Health Care Built Environment” (m-HCBE) addresses this relationship. Methods: A focused ethnographic study was conducted, including observation and focus groups. Architectural drawing files used to create distance matrices and isovist field view analyses were compared to panoramic photographs and ethnographic data. Results: Neighborhoods comprised of corner configurations with maximized visibility enhanced team interactions as well as observation of patients, offering the greatest opportunity for informal situated macrocognitive interactions (SMIs). Conclusions: Results from this study support the intricate link between macrocognitive interactions and space syntax constructs within the HCBE. These findings help to advance the m-HCBE theory for improving physical space by designing new spaces or refining existing spaces, or for adapting IPT practices to maximize formal and informal SMI opportunities; this lays the groundwork for future research to improve safety and quality for patient and family care.

Over the past few years, mining data located in heterogeneous and geographically distributed sites have been designated as one of the key important issues. Loading distributed data into centralized location for mining interesting rules is not a good approach. This is because it violates common issues such as data privacy and it imposes network overheads. The situation becomes worse when the network has limited bandwidth which is the case in most of the real time systems. This has prompted the need for intelligent data analysis to discover the hidden information in these huge amounts of distributed databases. In this research, we present an incremental approach for building an efficient Multi-Agent based algorithm for mining real world databases in geographically distributed sites. First, we propose the Distributed Multi-Agent Association Rules algorithm (DMAAR) to minimize the all-to-all broadcasting between distributed sites. Analytical calculations show that DMAAR reduces the algorithm complexity and minimizes the message communication cost. The proposed Multi-Agent based algorithm complies with the Foundation for Intelligent Physical Agents (FIPA), which is considered as the global standards in communication between agents, thus, enabling the proposed algorithm agents to cooperate with other standard agents. Second, the BitTable Multi-Agent Association Rules algorithm (BMAAR) is proposed. BMAAR includes an efficient BitTable data structure which helps in compressing the database thus can easily fit into the memory of the local sites. It also includes two BitWise AND/OR operations for quick candidate itemsets generation and support counting. Moreover, the algorithm includes three transaction trimming techniques to reduce the size of the mined data. Third, we propose the Pruning Multi-Agent Association Rules algorithm (PMAAR) which includes three candidate itemsets pruning techniques for reducing the large number of generated candidate itemsets, consequently, reducing the total time for the mining process. The proposed PMAAR algorithm has been compared with existing Association Rules algorithms against different benchmark datasets and has proved to have better performance and execution time. Moreover, PMAAR has been implemented on real world distributed medical databases obtained from more than one hospital in Egypt to discover the hidden Association Rules in patients' records to demonstrate the merits and capabilities of the proposed model further. Medical data was anonymously obtained without the patients' personal details. The analysis helped to identify the existence or the absence of the disease based on minimum number of effective examinations and tests. Thus, the proposed algorithm can help in providing accurate medical decisions based on cost effective treatments, improving the medical service for the patients, reducing the real time response for the health system and improving the quality of clinical decision making.

Over the past few years, mining data located in heterogeneous and geographically distributed sites have been designated as one of the key important issues. Loading distributed data into centralized location for mining interesting rules is not a good approach. This is because it violates common issues such as data privacy and it imposes network overheads. The situation becomes worse when the network has limited bandwidth which is the case in most of the real time systems. This has prompted the need for intelligent data analysis to discover the hidden information in these huge amounts of distributed databases. In this research, we present an incremental approach for building an efficient Multi-Agent based algorithm for mining real world databases in geographically distributed sites. First, we propose the Distributed Multi-Agent Association Rules algorithm (DMAAR) to minimize the all-to-all broadcasting between distributed sites. Analytical calculations show that DMAAR reduces the algorithm complexity and minimizes the message communication cost. The proposed Multi-Agent based algorithm complies with the Foundation for Intelligent Physical Agents (FIPA), which is considered as the global standards in communication between agents, thus, enabling the proposed algorithm agents to cooperate with other standard agents. Second, the BitTable Multi-Agent Association Rules algorithm (BMAAR) is proposed. BMAAR includes an efficient BitTable data structure which helps in compressing the database thus can easily fit into the memory of the local sites. It also includes two BitWise AND/OR operations for quick candidate itemsets generation and support counting. Moreover, the algorithm includes three transaction trimming techniques to reduce the size of the mined data. Third, we propose the Pruning Multi-Agent Association Rules algorithm (PMAAR) which includes three candidate itemsets pruning techniques for reducing the large number of generated candidate itemsets, consequently, reducing the total time for the mining process. The proposed PMAAR algorithm has been compared with existing Association Rules algorithms against different benchmark datasets and has proved to have better performance and execution time. Moreover, PMAAR has been implemented on real world distributed medical databases obtained from more than one hospital in Egypt to discover the hidden Association Rules in patients¿ records to demonstrate the merits and capabilities of the proposed model further. Medical data was anonymously obtained without the patients¿ personal details. The analysis helped to identify the existence or the absence of the disease based on minimum number of effective examinations and tests. Thus, the proposed algorithm can help in providing accurate medical decisions based on cost effective treatments, improving the medical service for the patients, reducing the real time response for the health system and improving the quality of clinical decision making.

Le sepsis tardif, défini comme une infection systémique chez les nouveaux nés âgés de plus de 3 jours, survient chez environ 7% à 10% de tous les nouveau-nés et chez plus de 25% des nouveau-nés de très faible poids de naissance qui sont hospitalisés dans les unités de soins intensifs néonatals (USIN). Les apnées et bradycardies (AB) spontanées récurrentes et graves sont parmi les principaux indicateurs précoces cliniques de l'infection systémique chez les prématurés. L'objectif de cette thèse est de déterminer si la variabilité du rythme cardiaque (VRC), la respiration et l'analyse de leurs relations aident au diagnostic de l'infection chez les nouveaux nés prématurés par des moyens non invasifs en USIN. Par conséquent, on a effectué l'analyse Mono-Voie (MV) et Bi-Voies (BV) sur deux groupes sélectionnés de nouveau-nés prématurés: sepsis (S) vs. non-sepsis (NS). (1) Tout d'abord, on a étudié la série RR non seulement par des méthodes de distribution (moy, varn, skew, kurt, med, SpAs), par les méthodes linéaire: le domaine temporel (SD, RMSSD) et dans le domaine fréquentiel (p_VLF, p_LF, p_HF), mais aussi par les méthodes non–linéaires: la théorie du chaos (alphas, alphaF) et la théorie de l'information (AppEn, SamEn, PermEn, Regul). Pour chaque méthode, nous étudions trois tailles de fenêtre 1024/2048/4096, puis nous comparons ces méthodes afin de trouver les meilleures façons de distinguer S de NS. Les résultats montrent que les indices alphaS, alphaF et SamEn sont les paramètres optimaux pour séparer les deux populations. (2) Ensuite, la question du couplage fonctionnel entre la VRC et la respiration nasale est adressée. Des relations linéaires et non-linéaires ont été explorées. Les indices linéaires sont la corrélation (r²), l'indice de la fonction de cohérence (Cohere) et la corrélation temps-fréquence (r2t,f) , tandis que le coefficient de régression non-linéaire (h²) a été utilisé pour analyser des relations non-linéaires. Nous avons calculé les deux directions de couplage pendant l'évaluation de l'indice h2 de régression non-linéaire. Enfin, à partir de l'ensemble du processus d'analyse, il est évident que les trois indices (r2tf_rn_raw_0p2_0p4, h2_rn_raw et h2_nr_raw) sont des moyens complémentaires pour le diagnostic du sepsis de façon non-invasive chez ces patients fragiles. (3) Après, l'étude de faisabilité de la détection du sepsis en USIN est réalisée sur la base des paramètres retenus lors des études MV et BV. Nous avons montré que le test proposé, basé sur la fusion optimale des six indices ci-dessus, conduit à de bonnes performances statistiques. En conclusion, les mesures choisies lors de l'analyse des signaux en MV et BV ont une bonne répétabilité et permettent de mettre en place un test en vue du diagnostic non invasif et précoce du sepsis. Le test proposé peut être utilisé pour fournir une alarme fiable lors de la survenue d'un épisode d'AB tout en exploitant les systèmes de monitoring actuels en USIN. / Late-onset sepsis, defined as a systemic infection in neonates older than 3 days, occurs in approximately 10% of all neonates and in more than 25% of very low birth weight infants who are hospitalized in Neonatal Intensive Care Units (NICU). Recurrent and severe spontaneous apneas and bradycardias (AB) is one of the major clinical early indicators of systemic infection in the premature infant. Various hematological and biochemical markers have been evaluated for this indication but they are invasive procedures that cannot be repeated several times. The objective of this Ph.D dissertation was to determine if heart rate variability (HRV), respiration and the analysis of their relationships help to the diagnosis of infection in premature infants via non-invasive ways in NICU. Therefore, we carried out Mono-Channel (MC) and Bi-Channel (BC) Analysis in two selected groups of premature infants: sepsis (S) vs. non-sepsis (NS). (1) Firstly, we studied the RR series not only by distribution methods (moy, varn, skew, kurt, med, SpAs), by linear methods: time domain (SD, RMSSD) and frequency domain (p_VLF, p_LF, p_HF), but also by non-linear methods: chaos theory (alphaS, alphaF) and information theory (AppEn, SamEn, PermEn, Regul). For each method, we attempt three sizes of window 1024/2048/4096, and then compare these methods in order to find the optimal ways to distinguish S from NS. The results show that alphaS, alphaF and SamEn are optimal parameters to recognize sepsis from the diagnosis of late neonatal infection in premature infants with unusual and recurrent AB. (2) The question about the functional coupling of HRV and nasal respiration is addressed. Linear and non-linear relationships have been explored. Linear indexes were correlation (r²), coherence function (Cohere) and time-frequency index (r2t,f), while a non-linear regression coefficient (h²) was used to analyze non-linear relationships. We calculated two directions during evaluate the index h2 of non-linear regression. Finally, from the entire analysis process, it is obvious that the three indexes (r2tf_rn_raw_0p2_0p4, h2_rn_raw and h2_nr_raw) were complementary ways to diagnosticate sepsis in a non-invasive way, in such delicate patients.(3) Furthermore, feasibility study is carried out on the candidate parameters selected from MC and BC respectively. We discovered that the proposed test based on optimal fusion of 6 features shows good performance with the largest Area Under Curves (AUC) and the least Probability of False Alarm (PFA). As a conclusion, we believe that the selected measures from MC and BC signal analysis have a good repeatability and accuracy to test for the diagnosis of sepsis via non-invasive NICU monitoring system, which can reliably confirm or refute the diagnosis of infection at an early stage.

Service provision in veterinary oncology in Ontario was examined using a mixed methods approach. First, an interview-based qualitative study explored the service expectations of oncology clients at a tertiary referral centre. Next, a survey-based quantitative study established an understanding of oncology service in primary care practice and investigated the treatment recommendations of practitioners for dogs diagnosed with cancer. The first study, which involved 30 individual and dyadic interviews, identified “uncertainty” (attributable to the unpredictable nature of cancer and its treatment) as an overarching psychological feature of clients’ experience. Consequently, “the communication of information” (both content and process) was the foremost service expectation. For clients, it enabled confidence in the service, the ability to make informed patient care decisions, and preparedness for the potential outcomes of those decisions; it also contributed to creating a humanistic environment, which enhanced client resiliency. Findings suggest that services can support client efforts to manage uncertainty through strategic design and delivery of service, and incorporate intentional communication strategies to support clients’ psychological fortitude in managing the cancer journey. The second study, a vignette-based survey of primary care practitioners across Ontario (N=1071) which investigated veterinarian decision-making in relation to oncology care, determined that 56% of practitioners recommended referral as their first choice of intervention, while 28% recommended palliative care, 13% in-clinic treatment, and 3% euthanasia. Recommendations were associated with patient, client and veterinarian factors. Specifically, referral and treatment were recommended for younger dogs, healthier dogs, and dogs with lymphoma versus osteosarcoma; for strongly bonded clients, and financially secure clients; and by veterinarians who graduated from a North American college, had experience with treating cancer, felt confident in the referral centre, and believed treatment was worthwhile, with variation in relation to practitioner gender and the type of medicine practiced. The human-animal bond appeared to be the primary factor associated with practitioners’ advocacy for quality of medical care for patients. Through a blend of qualitative and quantitative methodologies, this thesis contributes to the evidence upon which best practices may be built so as to enhance the quality of patient and client care in veterinary oncology. / Ontario Veterinary College Pet Trust Fund 049406 and 049854