[Potential of AI for the Treatment of Acute Respiratory Distress Syndrome (ARDS)]. / Potenzial von KI für die Behandlung des akuten Lungenversagens (ARDS).

Acute respiratory distress syndrome (ARDS) is still associated with high mortality rates and poses a significant, vital threat to ICU patients because this syndrome is often detected too late (or not at all), and timely therapy and the fastest possible elimination of the underlying causes thus fail to materialize. Artificial Intelligence (AI) solutions can enable clinicians to make every minute in the ICU work for the patient by processing and analyzing all relevant data, thus supporting early diagnosis, adhering to clinical guidelines, and even providing a prognosis for the course of the ICU. This article shows what is already possible and where further challenges lie in this field of digital medicine.

Impacts of vascular comorbidities on free flap perfusion in microvascular head and neck reconstruction.

PURPOSE: Arterial hypertension (AHTN), type 2 diabetes mellitus (DM), and atherosclerotic vascular disease (ASVD) are common vascular comorbidities in patients undergoing reconstruction of the head and neck region with a microvascular free flap. These conditions may affect flap perfusion (microvascular blood flow and tissue oxygenation), which is a prerequisite for flap survival and thus reconstruction success. This study aimed to investigate the impacts of AHTN, DM, and ASVD on flap perfusion. METHODS: Data from 308 patients who underwent successful reconstruction of the head and neck region with radial free forearm flaps, anterolateral thigh flaps, or fibula free flaps between 2011 and 2020 were retrospectively analyzed. Flap perfusion was measured intraoperatively and postoperatively with the O2C tissue oxygen analysis system. Flap blood flow, hemoglobin concentration, and hemoglobin oxygen saturation were compared between patients with and without AHTN, DM, and ASVD. RESULTS: Intraoperative hemoglobin oxygen saturation and postoperative blood flow were lower in patients with ASVD than in patients without ASVD (63.3% vs. 69.5%, p = 0.046; 67.5 arbitrary units [AU] vs. 85.0 AU, p = 0.036; respectively). These differences did not persist in the multivariable analysis (all p > 0.05). No difference was found in intraoperative or postoperative blood flow or hemoglobin oxygen saturation between patients with and without AHTN or DM (all p > 0.05). CONCLUSION: Perfusion of microvascular free flaps used for head and neck reconstruction is not impaired in patients with AHTN, DM, or ASVD. Unrestricted flap perfusion may contribute to the observed successful use of microvascular free flaps in patients with these comorbidities.

[Non-invasive Mechanical Ventilation in Acute Respiratory Failure. Clinical Practice Guidelines - on behalf of the German Society of Pneumology and Ventilatory Medicine]. / S2k-Leitlinie Nichtinvasive Beatmung als Therapie der akuten respiratorischen Insuffizienz.

The guideline update outlines the advantages as well as the limitations of NIV in the treatment of acute respiratory failure in daily clinical practice and in different indications.Non-invasive ventilation (NIV) has a high value in therapy of hypercapnic acute respiratory failure, as it significantly reduces the length of ICU stay and hospitalization as well as mortality.Patients with cardiopulmonary edema and acute respiratory failure should be treated with continuous positive airway pressure (CPAP) and oxygen in addition to necessary cardiological interventions. This should be done already prehospital and in the emergency department.In case of other forms of acute hypoxaemic respiratory failure with only mild or moderately disturbed gas exchange (PaO2/FiO2 > 150 mmHg) there is no significant advantage or disadvantage compared to high flow nasal oxygen (HFNO). In severe forms of ARDS NIV is associated with high rates of treatment failure and mortality, especially in cases with NIV-failure and delayed intubation.NIV should be used for preoxygenation before intubation. In patients at risk, NIV is recommended to reduce extubation failure. In the weaning process from invasive ventilation NIV essentially reduces the risk of reintubation in hypercapnic patients. NIV is regarded useful within palliative care for reduction of dyspnea and improving quality of life, but here in concurrence to HFNO, which is regarded as more comfortable. Meanwhile NIV is also recommended in prehospital setting, especially in hypercapnic respiratory failure and pulmonary edema.With appropriate monitoring in an intensive care unit NIV can also be successfully applied in pediatric patients with acute respiratory insufficiency.

Differences and Similarities Among COVID-19 Patients Treated in Seven ICUs in Three Countries Within One Region: An Observational Cohort Study.

OBJECTIVES: To investigate healthcare system-driven variation in general characteristics, interventions, and outcomes in coronavirus disease 2019 (COVID-19) patients admitted to the ICU within one Western European region across three countries. DESIGN: Multicenter observational cohort study. SETTING: Seven ICUs in the Euregio Meuse-Rhine, one region across Belgium, The Netherlands, and Germany. PATIENTS: Consecutive COVID-19 patients supported in the ICU during the first pandemic wave. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: Baseline demographic and clinical characteristics, laboratory values, and outcome data were retrieved after ethical approval and data-sharing agreements. Descriptive statistics were performed to investigate country-related practice variation. From March 2, 2020, to August 12, 2020, 551 patients were admitted. Mean age was 65.4 ± 11.2 years, and 29% were female. At admission, Acute Physiology and Chronic Health Evaluation II scores were 15.0 ± 5.5, 16.8 ± 5.5, and 15.8 ± 5.3 (p = 0.002), and Sequential Organ Failure Assessment scores were 4.4 ± 2.7, 7.4 ± 2.2, and 7.7 ± 3.2 (p < 0.001) in the Belgian, Dutch, and German parts of Euregio, respectively. The ICU mortality rate was 22%, 42%, and 44%, respectively (p < 0.001). Large differences were observed in the frequency of organ support, antimicrobial/inflammatory therapy application, and ICU capacity. Mixed-multivariable logistic regression analyses showed that differences in ICU mortality were independent of age, sex, disease severity, comorbidities, support strategies, therapies, and complications. CONCLUSIONS: COVID-19 patients admitted to ICUs within one region, the Euregio Meuse-Rhine, differed significantly in general characteristics, applied interventions, and outcomes despite presumed genetic and socioeconomic background, admission diagnosis, access to international literature, and data collection are similar. Variances in healthcare systems' organization, particularly ICU capacity and admission criteria, combined with a rapidly spreading pandemic might be important drivers for the observed differences. Heterogeneity between patient groups but also healthcare systems should be presumed to interfere with outcomes in coronavirus disease 2019.

[Medical students as helpers in the pandemic : Innovative concept for recruitment, training and assignment planning of medical students as medical personnel during the COVID-19 pandemic]. / Medizinstudierende als Helfende in der Pandemie : Innovatives Konzept zu Rekrutierung, Schulung und Einsatzplanung von Medizinstudierenden als medizinisches Personal während der COVID-19-Pandemie.

BACKGROUND: The COVID-19 pandemic posed enormous challenges to the German healthcare system and highlighted the need for strategies to recruit, train, and deploy medical personnel. Until now, no holistic concept existed to use medical students as support for professionals in intensive care units (ICU) to avoid staff shortages in medical care. METHOD: In a large-scale pilot project 265 medical students were trained for an ICU assignment. The innovative training module was accompanied by a pre-post questionnaire for self-assessment of the skills learned. 22 weeks after the training module and still during the pandemic deployment, another questionnaire was used to evaluate experiences in deployment and the efficiency of the training module with respect to preparation for ICU deployment. RESULTS: The analysis revealed significant mean differences for all COVID-19-specific variables (safety dimension) in favor of the training module (n = 168). The deployment evaluation showed that the training concept was inconsistently assessed as preparation for the work deployment for 69 of the 89 deployed students in total (53% agreement/47% disagreement). CONCLUSION: The results show a good feasibility of an innovative training concept for medical students with respect to a pandemic deployment as assistants in intensive care units. The concept is suitable for providing additional helpers in intensive care units during a pandemic; however, the inconsistent evaluation indicates that the concept can be expanded and needs to be adapted.

[Usage of Artificial Intelligence in the Combat against the COVID-19 Pandemic]. / Nutzung von künstlicher Intelligenz zur Bekämpfung der COVID-19-Pandemie.

The COVID-19 pandemic is a global health emergency of historic dimension. In this situation, researchers worldwide wanted to help manage the pandemic by using artificial intelligence (AI). This narrative review aims to describe the usage of AI in the combat against COVID-19. The addressed aspects encompass AI algorithms for analysis of thoracic X-rays or CTs, prediction models for severity and outcome of the disease, AI applications in development of new drugs and vaccines as well as forecasting models for spread of the virus. The review shows, which approaches were pursued, and which were successful.

[Artificial Intelligence: Challenges and Applications in Intensive Care Medicine]. / Künstliche Intelligenz: Herausforderungen und Nutzen in der Intensivmedizin.

The high workload in intensive care medicine arises from the exponential growth of medical knowledge, the flood of data generated by the permanent and intensive monitoring of intensive care patients, and the documentation burden. Artificial intelligence (AI) is predicted to have a great impact on ICU work in the near future as it will be applicable in many areas of critical care medicine. These applications include documentation through speech recognition, predictions for decision support, algorithms for parameter optimisation and the development of personalised intensive care medicine. AI-based decision support systems can augment human therapy decisions. Primarily through machine learning, a sub-discipline of AI, self-adaptive algorithms can learn to recognise patterns and make predictions. For actual use in clinical settings, the explainability of such systems is a prerequisite. Intensive care staff spends a large amount of their working hours on documentation, which has increased up to 50% of work time with the introduction of PDMS. Speech recognition has the potential to reduce this documentation burden. It is not yet precise enough to be usable in the clinic. The application of AI in medicine, with the help of large data sets, promises to identify diagnoses more quickly, develop individualised, precise treatments, support therapeutic decisions, use resources with maximum effectiveness and thus optimise the patient experience in the near future.

Biometric covariates and outcome in COVID-19 patients: are we looking close enough?

BACKGROUND: The impact of biometric covariates on risk for adverse outcomes of COVID-19 disease was assessed by numerous observational studies on unstratified cohorts, which show great heterogeneity. However, multilevel evaluations to find possible complex, e.g. non-monotonic multi-variate patterns reflecting mutual interference of parameters are missing. We used a more detailed, computational analysis to investigate the influence of biometric differences on mortality and disease evolution among severely ill COVID-19 patients. METHODS: We analyzed a group of COVID-19 patients requiring Intensive care unit (ICU) treatment. For further analysis, the study group was segmented into six subgroups according to Body mass index (BMI) and age. To link the BMI/age derived subgroups with risk factors, we performed an enrichment analysis of diagnostic parameters and comorbidities. To suppress spurious patterns, multiple segmentations were analyzed and integrated into a consensus score for each analysis step. RESULTS: We analyzed 81 COVID-19 patients, of whom 67 required mechanical ventilation (MV). Mean mortality was 35.8%. We found a complex, non-monotonic interaction between age, BMI and mortality. A subcohort of patients with younger age and intermediate BMI exhibited a strongly reduced mortality risk (p < 0.001), while differences in all other groups were not significant. Univariate impacts of BMI or age on mortality were missing. Comparing MV with non-MV patients, we found an enrichment of baseline CRP, PCT and D-Dimers within the MV group, but not when comparing survivors vs. non-survivors within the MV patient group. CONCLUSIONS: The aim of this study was to get a more detailed insight into the influence of biometric covariates on the outcome of COVID-19 patients with high degree of severity. We found that survival in MV is affected by complex interactions of covariates differing to the reported covariates, which are hidden in generic, non-stratified studies on risk factors. Hence, our study suggests that a detailed, multivariate pattern analysis on larger patient cohorts reflecting the specific disease stages might reveal more specific patterns of risk factors supporting individually adapted treatment strategies.

Machine learning identifies ICU outcome predictors in a multicenter COVID-19 cohort.

BACKGROUND: Intensive Care Resources are heavily utilized during the COVID-19 pandemic. However, risk stratification and prediction of SARS-CoV-2 patient clinical outcomes upon ICU admission remain inadequate. This study aimed to develop a machine learning model, based on retrospective & prospective clinical data, to stratify patient risk and predict ICU survival and outcomes. METHODS: A Germany-wide electronic registry was established to pseudonymously collect admission, therapeutic and discharge information of SARS-CoV-2 ICU patients retrospectively and prospectively. Machine learning approaches were evaluated for the accuracy and interpretability of predictions. The Explainable Boosting Machine approach was selected as the most suitable method. Individual, non-linear shape functions for predictive parameters and parameter interactions are reported. RESULTS: 1039 patients were included in the Explainable Boosting Machine model, 596 patients retrospectively collected, and 443 patients prospectively collected. The model for prediction of general ICU outcome was shown to be more reliable to predict "survival". Age, inflammatory and thrombotic activity, and severity of ARDS at ICU admission were shown to be predictive of ICU survival. Patients' age, pulmonary dysfunction and transfer from an external institution were predictors for ECMO therapy. The interaction of patient age with D-dimer levels on admission and creatinine levels with SOFA score without GCS were predictors for renal replacement therapy. CONCLUSIONS: Using Explainable Boosting Machine analysis, we confirmed and weighed previously reported and identified novel predictors for outcome in critically ill COVID-19 patients. Using this strategy, predictive modeling of COVID-19 ICU patient outcomes can be performed overcoming the limitations of linear regression models. Trial registration "ClinicalTrials" (clinicaltrials.gov) under NCT04455451.

Future Medical Artificial Intelligence Application Requirements and Expectations of Physicians in German University Hospitals: Web-Based Survey.

BACKGROUND: The increasing development of artificial intelligence (AI) systems in medicine driven by researchers and entrepreneurs goes along with enormous expectations for medical care advancement. AI might change the clinical practice of physicians from almost all medical disciplines and in most areas of health care. While expectations for AI in medicine are high, practical implementations of AI for clinical practice are still scarce in Germany. Moreover, physicians' requirements and expectations of AI in medicine and their opinion on the usage of anonymized patient data for clinical and biomedical research have not been investigated widely in German university hospitals. OBJECTIVE: This study aimed to evaluate physicians' requirements and expectations of AI in medicine and their opinion on the secondary usage of patient data for (bio)medical research (eg, for the development of machine learning algorithms) in university hospitals in Germany. METHODS: A web-based survey was conducted addressing physicians of all medical disciplines in 8 German university hospitals. Answers were given using Likert scales and general demographic responses. Physicians were asked to participate locally via email in the respective hospitals. RESULTS: The online survey was completed by 303 physicians (female: 121/303, 39.9%; male: 173/303, 57.1%; no response: 9/303, 3.0%) from a wide range of medical disciplines and work experience levels. Most respondents either had a positive (130/303, 42.9%) or a very positive attitude (82/303, 27.1%) towards AI in medicine. There was a significant association between the personal rating of AI in medicine and the self-reported technical affinity level (H4=48.3, P<.001). A vast majority of physicians expected the future of medicine to be a mix of human and artificial intelligence (273/303, 90.1%) but also requested a scientific evaluation before the routine implementation of AI-based systems (276/303, 91.1%). Physicians were most optimistic that AI applications would identify drug interactions (280/303, 92.4%) to improve patient care substantially but were quite reserved regarding AI-supported diagnosis of psychiatric diseases (62/303, 20.5%). Of the respondents, 82.5% (250/303) agreed that there should be open access to anonymized patient databases for medical and biomedical research. CONCLUSIONS: Physicians in stationary patient care in German university hospitals show a generally positive attitude towards using most AI applications in medicine. Along with this optimism comes several expectations and hopes that AI will assist physicians in clinical decision making. Especially in fields of medicine where huge amounts of data are processed (eg, imaging procedures in radiology and pathology) or data are collected continuously (eg, cardiology and intensive care medicine), physicians' expectations of AI to substantially improve future patient care are high. In the study, the greatest potential was seen in the application of AI for the identification of drug interactions, assumedly due to the rising complexity of drug administration to polymorbid, polypharmacy patients. However, for the practical usage of AI in health care, regulatory and organizational challenges still have to be mastered.

Favorable COVID-19 course despite significant comorbidities in a ruxolitinib-treated patient with primary myelofibrosis.

COVID-19 carries a high risk of severe disease course, particularly in patients with comorbidities. Therapy of severe COVID-19 infection has relied on supportive intensive care measures. More specific approaches including drugs that limit the detrimental "cytokine storm", such as Janus-activated kinase (JAK) inhibitors, are being discussed. Here, we report a compelling case of a 55-yo patient with proven COVID-19 pneumonia, who was taking the JAK1/2 inhibitor ruxolitinib in-label for co-existing primary myelofibrosis for 15 months prior to coronavirus infection. The patient had significant comorbidities, including chronic kidney disease, arterial hypertension, and obesity, and our previous cohort suggested that he was thus at high risk for acute respiratory distress syndrome (ARDS) and death from COVID-19. Since abrupt discontinuation of ruxolitinib may cause fatal cytokine storm and ARDS, ruxolitinib treatment was continued and was well tolerated, and the patient´s condition remained stable, without the need for mechanical ventilation or vasopressors. The patient became negative for SARS-CoV-2 and was discharged home after 15 days. In conclusion, our report provides clinical evidence that ruxolitinib treatment is feasible and can be beneficial in patients with COVID-19 pneumonia, preventing cytokine storm and ARDS.

Future Mobile Device Usage, Requirements, and Expectations of Physicians in German University Hospitals: Web-Based Survey.

BACKGROUND: The use of mobile devices in hospital care constantly increases. However, smartphones and tablets have not yet widely become official working equipment in medical care. Meanwhile, the parallel use of private and official devices in hospitals is common. Medical staff use smartphones and tablets in a growing number of ways. This mixture of devices and how they can be used is a challenge to persons in charge of defining strategies and rules for the usage of mobile devices in hospital care. OBJECTIVE: Therefore, we aimed to examine the status quo of physicians' mobile device usage and concrete requirements and their future expectations of how mobile devices can be used. METHODS: We performed a web-based survey among physicians in 8 German university hospitals from June to October 2019. The online survey was forwarded by hospital management personnel to physicians from all departments involved in patient care at the local sites. RESULTS: A total of 303 physicians from almost all medical fields and work experience levels completed the web-based survey. The majority regarded a tablet (211/303, 69.6%) and a smartphone (177/303, 58.4%) as the ideal devices for their operational area. In practice, physicians are still predominantly using desktop computers during their worktime (mean percentage of worktime spent on a desktop computer: 56.8%; smartphone: 12.8%; tablet: 3.6%). Today, physicians use mobile devices for basic tasks such as oral (171/303, 56.4%) and written (118/303, 38.9%) communication and to look up dosages, diagnoses, and guidelines (194/303, 64.0%). Respondents are also willing to use mobile devices for more advanced applications such as an early warning system (224/303, 73.9%) and mobile electronic health records (211/303, 69.6%). We found a significant association between the technical affinity and the preference of device in medical care (χs2=53.84, P<.001) showing that with increasing self-reported technical affinity, the preference for smartphones and tablets increases compared to desktop computers. CONCLUSIONS: Physicians in German university hospitals have a high technical affinity and positive attitude toward the widespread implementation of mobile devices in clinical care. They are willing to use official mobile devices in clinical practice for basic and advanced mobile health uses. Thus, the reason for the low usage is not a lack of willingness of the potential users. Challenges that hinder the wider adoption of mobile devices might be regulatory, financial and organizational issues, and missing interoperability standards of clinical information systems, but also a shortage of areas of application in which workflows are adapted for (small) mobile devices.

[Sepsis Update: Evidence-Based Therapy]. / Sepsis-Update: evidenzbasierte Therapie.

Like polytrauma, acute myocardial infarction or stroke, sepsis and septic shock are medical emergencies that require adequate early detection and immediate, targeted management. The therapy of sepsis and septic shock has developed continuously in recent years. This article provides an overview of the current evidence of sepsis and septic shock therapy and its implementation into clinical practice.

Impact of multidrug-resistant bacteria on outcome in patients with prolonged weaning.

BACKGROUND: Pneumonia and septic pneumonic shock are the most common indications for long-term mechanical ventilation and prolonged weaning, independent of any comorbidities. Multidrug resistant (MDR) bacteria are emerging as a cause of pneumonia or occur as a consequence of antimicrobial therapy. The influence of MDR bacteria on outcomes in patients with prolonged weaning is unknown. METHODS: Patients treated in a specialized weaning unit of a university hospital between April 2013 and April 2016 were analyzed. Demographic data, clinical characteristics, length of stay (LOS) in the intensive care unit (ICU) and weaning unit, ventilator-free days and mortality rates were determined in prolonged weaning patients with versus without MDR bacteria (methicillin-resistant Staphylococcus aureus bacteria, [MRSA]; extended spectrum beta lactamase [ESBL]- and Gyrase-producing gram negative bacteria resistant to three of four antibiotic groups [3 MRGN]; panresistant Pseudomonas aeruginosa and other carbapenemase-producing gram-negative bacteria resistant to all four antibiotic groups [4 MRGN]). Weaning failure was defined as death or discharge with invasive ventilation. RESULTS: Of 666 patients treated in the weaning unit, 430 fulfilled the inclusion criteria and were included in the analysis. A total of 107 patients had isolates of MDR bacteria suspected as causative pathogens identified during the treatment process. Patients with MDR bacteria had higher SAPS II values at ICU admission and a significantly longer ICU LOS. Four MRGN P. aeruginosa and Acinetobacter baumanii were the most common MDR bacteria identified. Patients with versus without MDR bacteria had significantly higher arterial carbon dioxide levels at the time of weaning admission and a significantly lower rate of successful weaning (23% vs 31%, p < 0.05). Mortality rate on the weaning unit was 12.4% with no difference between the two patient groups. There were no significant differences between patient groups in secondary infections and ventilator-free days. CONCLUSIONS: In patients with pneumonia or septic pneumonic shock undergoing prolonged weaning, infection with MDR bacteria may influence the weaning success rate but does not appear to impact on patient survival.

Remote Ischemic Preconditioning Does Not Affect the Release of Humoral Factors in Propofol-Anesthetized Cardiac Surgery Patients: A Secondary Analysis of the RIPHeart Study.

In contrast to several smaller studies, which demonstrate that remote ischemic preconditioning (RIPC) reduces myocardial injury in patients that undergo cardiovascular surgery, the RIPHeart study failed to demonstrate beneficial effects of troponin release and clinical outcome in propofol-anesthetized cardiac surgery patients. Therefore, we addressed the potential biochemical mechanisms triggered by RIPC. This is a predefined prospective sub-analysis of the randomized and controlled RIPHeart study in cardiac surgery patients (n = 40) that was recently published. Blood samples were drawn from patients prior to surgery, after RIPC of four cycles of 5 min arm ischemia/5 min reperfusion (n = 19) and the sham (n = 21) procedure, after connection to cardiopulmonary bypass (CPB), at the end of surgery, 24 h postoperatively, and 48 h postoperatively for the measurement of troponin T, macrophage migration inhibitory factor (MIF), stromal cell-derived factor 1 (CXCL12), IL-6, CXCL8, and IL-10. After RIPC, right atrial tissue samples were taken for the measurement of extracellular-signal regulated kinase (ERK1/2), protein kinase B (AKT), Glycogen synthase kinase 3 (GSK-3ß), protein kinase C (PKCε), and MIF content. RIPC did not significantly reduce the troponin release when compared with the sham procedure. MIF serum levels intraoperatively increased, peaking at intensive care unit (ICU) admission (with an increase of 48.04%, p = 0.164 in RIPC; and 69.64%, p = 0.023 over the baseline in the sham procedure), and decreased back to the baseline 24 h after surgery, with no differences between the groups. In the right atrial tissue, MIF content decreased after RIPC (1.040 ± 1.032 Arbitrary units [au] in RIPC vs. 2.028 ± 1.631 [au] in the sham procedure, p < 0.05). CXCL12 serum levels increased significantly over the baseline at the end of surgery, with no differences between the groups. ERK1/2, AKT, GSK-3ß, and PKCɛ phosphorylation in the right atrial samples were no different between the groups. No difference was found in IL-6, CXCL8, and IL10 serum levels between the groups. In this cohort of cardiac surgery patients that received propofol anesthesia, we could not show a release of potential mediators of signaling, nor an effect on the inflammatory response, nor an activation of well-established protein kinases after RIPC. Based on these data, we cannot exclude that confounding factors, such as propofol, may have interfered with RIPC.

[Respiratory Insufficiency: State of the Art - Diagnosis and Therapy]. / Respiratorisches Versagen: State of the Art ­ Diagnose und Therapie.

The term "respiratory insufficiency" (RI) describes the inability of an organism to maintain the gas exchange between the ambient air and its peripheral organs. This causes a hypoxia and hypercapnia. The mechanisms that lead to RI are either an impaired gas exchange in the lung tissue or an alveolar hypoventilation caused by an insufficient ventilatory pump. Thus the RI is divided into a hypoxic or a hypercapnic RI. The diagnostic procedure for RI contains several examinations. One key aspect of the exploration is the identification of potential reversible and thus correctable reasons of the RI. The goal of the therapy is to maintain the oxygen supply for the peripheral organs and the elimination of CO2. It covers supportive and causal therapeutic interventions. Wherever possible, therapy should resolve the cause of the RI. The non-invasive ventilation (NIV) is the therapy of choice in severe cases of a hypercapnic RI. It relieves the exhausted respiratory muscles and improves respiratory situation. It can be used for hypoxic RI as well, but frequently invasive ventilation is required. If NIV is not able to improve the patient's condition, invasive ventilation (IV) is applied.

Electrical impedance tomography for predicting failure of spontaneous breathing trials in patients with prolonged weaning.

BACKGROUND: Spontaneous breathing trials (SBTs) on a T-piece can be difficult in patients with prolonged weaning because of remaining de-recruitment phenomena and/or insufficient ventilation. There is no clinically established method existent other than experience for estimating whether an SBT is most probably beneficial. Electrical impedance tomography (EIT) is a clinical useful online monitoring technique during mechanical ventilation, particularly because it enables analysis of effects of regional ventilation distribution. The aim of our observational study was to examine if EIT can predict whether patients with prolonged weaning will benefit from a planned SBT. METHODS: Thirty-one patients were examined. Blood gas analysis, vital parameter measurements, and EIT recordings were performed at three time points: (1) baseline with pressure support ventilation (PSV) (t0), (2) during a T-piece trial (t1), and (3) after resumption of PSV (t2). Calculation of EIT parameters was performed, including the impedance ratio (IR), the tidal variation of impedance (TIV), the changes in end-expiratory lung impedance (ΔEELI), the global inhomogeneity index (GI), and the regional ventilation delay (RVD) index with use of different thresholds of the percentage inspiration time (RVD40, RVD60, RVD80). The predictive power of the baseline GI with regard to clinical impairment of an SBT was analyzed by means of ROC curves. Clinical deterioration was assumed when tidal volume was decreased by at least 20 ml after the T-piece trial, measured at t2. RESULTS: Partial pressure of arterial oxygen significantly decreased at t1 (71 ± 15 mmHg) compared with t0 (85 ± 17 mmHg, p < 0.05) and t2 (82 ± 18 mmHg, p < 0.05). The IR trended toward higher values during t1. At t1, TIV and ΔEELI significantly decreased. The GI was significantly increased at t1 (t0 59.3 ± 46.1 vs t1 81.5 ± 62.5, p = 0.001), as were all RVD indexes. Assuming a GI cutoff value of >40, sensitivity of 85% and specificity of 50% were reached for predicting an increased future tidal volume. CONCLUSIONS: EIT enables monitoring of regional ventilation distribution during SBTs and is suitable to estimate whether an SBT probably will be beneficial for an individual patient. Therefore, the application of EIT can support clinical decisions regarding patients in the phase of prolonged weaning.

[Weaning: more than terminating mechanical ventilation]. / Weaning - Mehr als nur Beatmung beenden.

Mechanical ventilation is an essential part of modern intensive care. It is used in patients with acute respiratory failure and, depending on the type of respiratory failure, different modes of application. In particular, invasive mechanical ventilation should be terminated as raidly as possible to avoid the associated risks.The major proportion of ventilated patients can be weaned from mechanical ventilation without problems after a short treatment period. In about 20% of ventilated patients, however, an extremely protracted and complex weaning process can be observed, even though the cause necessitating ventilation has long since been eliminated. In addition to the stages in the process of weaning from the ventilator, in particular the pathophysiological processes that lead to prolonged weaning are addressed in this article.

Development of a machine learning model for prediction of the duration of unassisted spontaneous breathing in patients during prolonged weaning from mechanical ventilation.

PURPOSE: Treatment of patients undergoing prolonged weaning from mechanical ventilation includes repeated spontaneous breathing trials (SBTs) without respiratory support, whose duration must be balanced critically to prevent over- and underload of respiratory musculature. This study aimed to develop a machine learning model to predict the duration of unassisted spontaneous breathing. MATERIALS AND METHODS: Structured clinical data of patients from a specialized weaning unit were used to develop (1) a classifier model to qualitatively predict an increase of duration, (2) a regressor model to quantitatively predict the precise duration of SBTs on the next day, and (3) the duration difference between the current and following day. 61 features, known to influence weaning, were included into a Histogram-based gradient boosting model. The models were trained and evaluated using separated data sets. RESULTS: 18.948 patient-days from 1018 individual patients were included. The classifier model yielded an ROC-AUC of 0.713. The regressor models displayed a mean absolute error of 2:50 h for prediction of absolute durations and 2:47 h for day-to-day difference. CONCLUSIONS: The developed machine learning model showed informed results when predicting the spontaneous breathing capacity of a patient in prolonged weaning, however lacking prognostic quality required for direct translation to clinical use.