Pneumonia and acute respiratory distress syndrome are common and important causes of respiratory failure in the intensive care unit with a significant impact on morbidity, mortality and health care utilization despite...Pneumonia and acute respiratory distress syndrome are common and important causes of respiratory failure in the intensive care unit with a significant impact on morbidity, mortality and health care utilization despite early antimicrobial therapy and lung protective mechanical ventilation. Both clinical entities are characterized by acute pulmonary inflammation in response to direct or indirect lung injury. Adjunct anti-inflammatory treatment with corticosteroids is increasingly used, although the evidence for benefit is limited. The treatment decisions are based on radiographic, clinical and physiological variables without regards to inflammatory state. Current evidence suggests a role of biomarkers for the assessment of severity, and distinguishing sub-phenotypes (hyperinflammatory versus hypo-inflammatory) with important prognostic and therapeutic implications. Although many inflammatory biomarkers have been studied the most common and of interest are C-reactive protein, procalcitonin, and pro-inflammatory cytokines including interleukin 6. While extensively studied as prognostic tools (prognostic enrichment), limited data are available for the role of biomarkers in determining appropriate initiation, timing and dosing of adjunct anti-inflammatory treatment (predictive enrichment)展开更多
Processes to ensure world-wide best-practice for critical care delivery is likely to minimize preventable death, disability and costly complications for any healthcare system's sickest patients, but no large-scale...Processes to ensure world-wide best-practice for critical care delivery is likely to minimize preventable death, disability and costly complications for any healthcare system's sickest patients, but no large-scale efforts have so far been undertaken towards these goals. The advances in medical informatics and human factors engineering have provided possibility for novel and user-friendly clinical decision support tools that can be applied in a complex and busy hospital setting. To facilitate timely and accurate best-practice delivery in critically ill patients international group of intensive care unit(ICU) physicians and researchers developed a simple decision support tool: Checklist for Early Recognition and Treatment of Acute Illness(CERTAIN). The tool has been refined and tested in high fidelity simulated clinical environment and has been shown to improve performance of clinical providers faced with simulated emergencies. The aim of this international educational intervention is to implement CERTAIN into clinical practice in hospital settings with variable resources(included those in low income countries) and evaluate the impact of the tool on the care processes and patient outcomes. To accomplish our aims, CERTAIN will be uniformly available on either mobile or fixed computing devices(as well as a backup paper version) and applied in a standardized manner in the ICUs of diverse hospitals. To ensure the effectiveness of the proposed intervention, access to CERTAIN is coupled with structured training of bedside ICU providers.展开更多
The number of patients receiving hematopoietic stem cell transplantation(HSCT) is rapidly rising worldwide. Despite substantial improvements in peri-transplant care, pulmonary complications resulting in respiratory fa...The number of patients receiving hematopoietic stem cell transplantation(HSCT) is rapidly rising worldwide. Despite substantial improvements in peri-transplant care, pulmonary complications resulting in respiratory failure remain a major contributor to morbidity and mortality in the post-transplant period, and represent a major barrier to the overall success of HSCT. Infectious complications include pneumonia due to bacteria, viruses, and fungi, and most commonly occur during neutropenia in the early post-transplant period. Non-infectious complications include idiopathic pneumonia syndrome, periengraftment respiratory distress syndrome, diffuse alveolar hemorrhage, pulmonary veno-occlusive disease, delayed pulmonary toxicity syndrome, cryptogenic organizing pneumonia, bronchiolitis obliterans syndrome, and post-transplant lymphoproliferative disorder. These complications have distinct clinical features and risk factors, occur at differing times following transplant, and contribute to morbidity and mortality.展开更多
Widespread implementation of electronic health records has led to the increased use of artificial intelligence(AI)and computer modeling in clinical medicine.The early recognition and treatment of critical illness are ...Widespread implementation of electronic health records has led to the increased use of artificial intelligence(AI)and computer modeling in clinical medicine.The early recognition and treatment of critical illness are central to good outcomes but are made difficult by,among other things,the complexity of the environment and the often non-specific nature of the clinical presentation.Increasingly,AI applications are being proposed as decision supports for busy or distracted clinicians,to address this challenge.Data driven“associative”AI models are built from retrospective data registries with missing data and imprecise timing.Associative AI models lack transparency,often ignore causal mechanisms,and,while potentially useful in improved prognostication,have thus far had limited clinical applicability.To be clinically useful,AI tools need to provide bedside clinicians with actionable knowledge.Explicitly addressing causal mechanisms not only increases validity and replicability of the model,but also adds transparency and helps gain trust from the bedside clinicians for real world use of AI models in teaching and patient care.展开更多
Artificial intelligence(AI)and digital twin models of various systems have long been used in industry to test products quickly and efficiently.Use of digital twins in clinical medicine caught attention with the develo...Artificial intelligence(AI)and digital twin models of various systems have long been used in industry to test products quickly and efficiently.Use of digital twins in clinical medicine caught attention with the development of Archimedes,an AI model of diabetes,in 2003.More recently,AI models have been applied to the fields of cardiology,endocrinology,and undergraduate medical education.The use of digital twins and AI thus far has focused mainly on chronic disease management,their application in the field of critical care medicine remains much less explored.In neurocritical care,current AI technology focuses on interpreting electroencephalography,monitoring intracranial pressure,and prognosticating outcomes.AI models have been developed to interpret electroencephalograms by helping to annotate the tracings,detecting seizures,and identifying brain activation in unresponsive patients.In this mini-review we describe the challenges and opportunities in building an actionable AI model pertinent to neurocritical care that can be used to educate the newer generation of clinicians and augment clinical decision making.展开更多
AIM: To calculate cost effectiveness of the treatment of critically ill patients in a medical intensive care unit(ICU) of a middle income country with limited access to ICU resources. METHODS: A prospective cohort stu...AIM: To calculate cost effectiveness of the treatment of critically ill patients in a medical intensive care unit(ICU) of a middle income country with limited access to ICU resources. METHODS: A prospective cohort study and economic evaluation of consecutive patients treated in a recently established medical ICU in Sarajevo, Bosnia and Herzegovina. A cost utility analysis of the intensive care of critically ill patients compared to the hospital ward treatment from the perspective of the health care system was subsequently performed. Incremental cost effectiveness was calculated using estimates of ICU vs non-ICU treatment effectiveness based on a formal systematic review of published studies. Decision analytic modeling was used to compare treatment alternatives. Sensitivity analyses of the key model parameters were performed.RESULTS: Out of 148 patients, seventy patients(47.2%) survived to one year after critical illness with a median quality of life index 0.64 [interquartile range(IQR)0.49-0.76]. Median number of life years gained per patient was 30(IQR 16-40) or 18 quality adjusted life years(QALYs)(IQR 7-28). The cost of treatment of critically ill patients varied between 1820 dollar and20109 dollar per hospital survivor and between 100 dollar and 2514 dollar per QALY saved. Mean factors that influenced costs were: Age, diagnostic category,ICU and hospital length of stay and number and type of diagnostic and therapeutic interventions. The incremental cost effectiveness ratio for ICU treatment was estimated at 3254 dollar per QALY corresponding to35% of per capita GDP or a Very Cost Effective category according to World Health Organization criteria.CONCLUSION: The ICU treatment of critically ill medica patients in a resource poor country is cost effective and compares favorably with other medical interventions.Public health authorities in low and middle income countries should encourage development of critical care services.展开更多
BACKGROUND The coronavirus disease 2019(COVID-19)course may be affected by environmental factors.Ecological studies previously suggested a link between climatological factors and COVID-19 fatality rates.However,indivi...BACKGROUND The coronavirus disease 2019(COVID-19)course may be affected by environmental factors.Ecological studies previously suggested a link between climatological factors and COVID-19 fatality rates.However,individual-level impact of these factors has not been thoroughly evaluated yet.AIM To study the association of climatological factors related to patient location with unfavorable outcomes in patients.METHODS In this observational analysis of the Society of Critical Care Medicine Discovery Viral Infection and Respiratory Illness Universal Study:COVID-19 Registry cohort,the latitudes and altitudes of hospitals were examined as a covariate for mortality within 28 d of admission and the length of hospital stay.Adjusting for baseline parameters and admission date,multivariable regression modeling was utilized.Generalized estimating equations were used to fit the models.RESULTS Twenty-two thousand one hundred eight patients from over 20 countries were evaluated.The median age was 62(interquartile range:49-74)years,and 54%of the included patients were males.The median age increased with increasing latitude as well as the frequency of comorbidities.Contrarily,the percentage of comorbidities was lower in elevated altitudes.Mortality within 28 d of hospital admission was found to be 25%.The median hospital-free days among all included patients was 20 d.Despite the significant linear relationship between mortality and hospital-free days(adjusted odds ratio(aOR)=1.39(1.04,1.86),P=0.025 for mortality within 28 d of admission;aOR=-1.47(-2.60,-0.33),P=0.011 for hospital-free days),suggesting that adverse patient outcomes were more common in locations further away from the Equator;the results were no longer significant when adjusted for baseline differences(aOR=1.32(1.00,1.74),P=0.051 for 28-day mortality;aOR=-1.07(-2.13,-0.01),P=0.050 for hospital-free days).When we looked at the altitude’s effect,we discovered that it demonstrated a non-linear association with mortality within 28 d of hospital admission(aOR=0.96(0.62,1.47),1.04(0.92,1.19),0.49(0.22,0.90),and 0.51(0.27,0.98),for the altitude points of 75 MASL,125 MASL,400 MASL,and 600 MASL,in comparison to the reference altitude of 148 m.a.s.l,respectively.P=0.001).We detected an association between latitude and 28-day mortality as well as hospital-free days in this worldwide study.When the baseline features were taken into account,however,this did not stay significant.CONCLUSION Our findings suggest that differences observed in previous epidemiological studies may be due to ecological fallacy rather than implying a causal relationship at the patient level.展开更多
文摘Pneumonia and acute respiratory distress syndrome are common and important causes of respiratory failure in the intensive care unit with a significant impact on morbidity, mortality and health care utilization despite early antimicrobial therapy and lung protective mechanical ventilation. Both clinical entities are characterized by acute pulmonary inflammation in response to direct or indirect lung injury. Adjunct anti-inflammatory treatment with corticosteroids is increasingly used, although the evidence for benefit is limited. The treatment decisions are based on radiographic, clinical and physiological variables without regards to inflammatory state. Current evidence suggests a role of biomarkers for the assessment of severity, and distinguishing sub-phenotypes (hyperinflammatory versus hypo-inflammatory) with important prognostic and therapeutic implications. Although many inflammatory biomarkers have been studied the most common and of interest are C-reactive protein, procalcitonin, and pro-inflammatory cytokines including interleukin 6. While extensively studied as prognostic tools (prognostic enrichment), limited data are available for the role of biomarkers in determining appropriate initiation, timing and dosing of adjunct anti-inflammatory treatment (predictive enrichment)
文摘Processes to ensure world-wide best-practice for critical care delivery is likely to minimize preventable death, disability and costly complications for any healthcare system's sickest patients, but no large-scale efforts have so far been undertaken towards these goals. The advances in medical informatics and human factors engineering have provided possibility for novel and user-friendly clinical decision support tools that can be applied in a complex and busy hospital setting. To facilitate timely and accurate best-practice delivery in critically ill patients international group of intensive care unit(ICU) physicians and researchers developed a simple decision support tool: Checklist for Early Recognition and Treatment of Acute Illness(CERTAIN). The tool has been refined and tested in high fidelity simulated clinical environment and has been shown to improve performance of clinical providers faced with simulated emergencies. The aim of this international educational intervention is to implement CERTAIN into clinical practice in hospital settings with variable resources(included those in low income countries) and evaluate the impact of the tool on the care processes and patient outcomes. To accomplish our aims, CERTAIN will be uniformly available on either mobile or fixed computing devices(as well as a backup paper version) and applied in a standardized manner in the ICUs of diverse hospitals. To ensure the effectiveness of the proposed intervention, access to CERTAIN is coupled with structured training of bedside ICU providers.
文摘The number of patients receiving hematopoietic stem cell transplantation(HSCT) is rapidly rising worldwide. Despite substantial improvements in peri-transplant care, pulmonary complications resulting in respiratory failure remain a major contributor to morbidity and mortality in the post-transplant period, and represent a major barrier to the overall success of HSCT. Infectious complications include pneumonia due to bacteria, viruses, and fungi, and most commonly occur during neutropenia in the early post-transplant period. Non-infectious complications include idiopathic pneumonia syndrome, periengraftment respiratory distress syndrome, diffuse alveolar hemorrhage, pulmonary veno-occlusive disease, delayed pulmonary toxicity syndrome, cryptogenic organizing pneumonia, bronchiolitis obliterans syndrome, and post-transplant lymphoproliferative disorder. These complications have distinct clinical features and risk factors, occur at differing times following transplant, and contribute to morbidity and mortality.
文摘Widespread implementation of electronic health records has led to the increased use of artificial intelligence(AI)and computer modeling in clinical medicine.The early recognition and treatment of critical illness are central to good outcomes but are made difficult by,among other things,the complexity of the environment and the often non-specific nature of the clinical presentation.Increasingly,AI applications are being proposed as decision supports for busy or distracted clinicians,to address this challenge.Data driven“associative”AI models are built from retrospective data registries with missing data and imprecise timing.Associative AI models lack transparency,often ignore causal mechanisms,and,while potentially useful in improved prognostication,have thus far had limited clinical applicability.To be clinically useful,AI tools need to provide bedside clinicians with actionable knowledge.Explicitly addressing causal mechanisms not only increases validity and replicability of the model,but also adds transparency and helps gain trust from the bedside clinicians for real world use of AI models in teaching and patient care.
基金Supported by the National Center for Advancing Translational Sciences,No.UL1 TR002377.
文摘Artificial intelligence(AI)and digital twin models of various systems have long been used in industry to test products quickly and efficiently.Use of digital twins in clinical medicine caught attention with the development of Archimedes,an AI model of diabetes,in 2003.More recently,AI models have been applied to the fields of cardiology,endocrinology,and undergraduate medical education.The use of digital twins and AI thus far has focused mainly on chronic disease management,their application in the field of critical care medicine remains much less explored.In neurocritical care,current AI technology focuses on interpreting electroencephalography,monitoring intracranial pressure,and prognosticating outcomes.AI models have been developed to interpret electroencephalograms by helping to annotate the tracings,detecting seizures,and identifying brain activation in unresponsive patients.In this mini-review we describe the challenges and opportunities in building an actionable AI model pertinent to neurocritical care that can be used to educate the newer generation of clinicians and augment clinical decision making.
文摘AIM: To calculate cost effectiveness of the treatment of critically ill patients in a medical intensive care unit(ICU) of a middle income country with limited access to ICU resources. METHODS: A prospective cohort study and economic evaluation of consecutive patients treated in a recently established medical ICU in Sarajevo, Bosnia and Herzegovina. A cost utility analysis of the intensive care of critically ill patients compared to the hospital ward treatment from the perspective of the health care system was subsequently performed. Incremental cost effectiveness was calculated using estimates of ICU vs non-ICU treatment effectiveness based on a formal systematic review of published studies. Decision analytic modeling was used to compare treatment alternatives. Sensitivity analyses of the key model parameters were performed.RESULTS: Out of 148 patients, seventy patients(47.2%) survived to one year after critical illness with a median quality of life index 0.64 [interquartile range(IQR)0.49-0.76]. Median number of life years gained per patient was 30(IQR 16-40) or 18 quality adjusted life years(QALYs)(IQR 7-28). The cost of treatment of critically ill patients varied between 1820 dollar and20109 dollar per hospital survivor and between 100 dollar and 2514 dollar per QALY saved. Mean factors that influenced costs were: Age, diagnostic category,ICU and hospital length of stay and number and type of diagnostic and therapeutic interventions. The incremental cost effectiveness ratio for ICU treatment was estimated at 3254 dollar per QALY corresponding to35% of per capita GDP or a Very Cost Effective category according to World Health Organization criteria.CONCLUSION: The ICU treatment of critically ill medica patients in a resource poor country is cost effective and compares favorably with other medical interventions.Public health authorities in low and middle income countries should encourage development of critical care services.
文摘BACKGROUND The coronavirus disease 2019(COVID-19)course may be affected by environmental factors.Ecological studies previously suggested a link between climatological factors and COVID-19 fatality rates.However,individual-level impact of these factors has not been thoroughly evaluated yet.AIM To study the association of climatological factors related to patient location with unfavorable outcomes in patients.METHODS In this observational analysis of the Society of Critical Care Medicine Discovery Viral Infection and Respiratory Illness Universal Study:COVID-19 Registry cohort,the latitudes and altitudes of hospitals were examined as a covariate for mortality within 28 d of admission and the length of hospital stay.Adjusting for baseline parameters and admission date,multivariable regression modeling was utilized.Generalized estimating equations were used to fit the models.RESULTS Twenty-two thousand one hundred eight patients from over 20 countries were evaluated.The median age was 62(interquartile range:49-74)years,and 54%of the included patients were males.The median age increased with increasing latitude as well as the frequency of comorbidities.Contrarily,the percentage of comorbidities was lower in elevated altitudes.Mortality within 28 d of hospital admission was found to be 25%.The median hospital-free days among all included patients was 20 d.Despite the significant linear relationship between mortality and hospital-free days(adjusted odds ratio(aOR)=1.39(1.04,1.86),P=0.025 for mortality within 28 d of admission;aOR=-1.47(-2.60,-0.33),P=0.011 for hospital-free days),suggesting that adverse patient outcomes were more common in locations further away from the Equator;the results were no longer significant when adjusted for baseline differences(aOR=1.32(1.00,1.74),P=0.051 for 28-day mortality;aOR=-1.07(-2.13,-0.01),P=0.050 for hospital-free days).When we looked at the altitude’s effect,we discovered that it demonstrated a non-linear association with mortality within 28 d of hospital admission(aOR=0.96(0.62,1.47),1.04(0.92,1.19),0.49(0.22,0.90),and 0.51(0.27,0.98),for the altitude points of 75 MASL,125 MASL,400 MASL,and 600 MASL,in comparison to the reference altitude of 148 m.a.s.l,respectively.P=0.001).We detected an association between latitude and 28-day mortality as well as hospital-free days in this worldwide study.When the baseline features were taken into account,however,this did not stay significant.CONCLUSION Our findings suggest that differences observed in previous epidemiological studies may be due to ecological fallacy rather than implying a causal relationship at the patient level.
