Intrapleural minocycline pleurodesis for bilateral pneumothorax due to septic pulmonary embolism: a case report.

Background: Pneumothorax is a rare but serious complication of septic pulmonary embolism (SPE). SPE is a life-threatening disorder wherein infected thrombi bring infarction of the terminal and small caliber parts of the pulmonary vasculature and develop multiple nodular and cavitary lesions. Interventions other than conservative chest tube drainage for pneumothorax due to SPE have rarely been reported. Here, we present a case of bilateral pneumothorax due to SPE treated with intrapleural minocycline pleurodesis. Case Description: A 72-year-old male patient previously diagnosed as esophageal carcinoma developed metachronous bilateral pneumothorax while treated for brain metastases. Based on blood cultures and chest computed tomography images, he was diagnosed with pneumothorax secondary to SPE due to methicillin-susceptible Staphylococcus aureus bacteremia. Bilateral chest tube drainage was instituted. Continuous air leakage was found bilaterally after chest tube placement. He was treated with broad-spectrum antibiotics based on the susceptibility profile and supportive treatment for sepsis. Approximately 3 weeks later, air leakage significantly reduced. We performed intrapleural minocycline pleurodesis bilaterally to prevent the recurrence of pneumothorax; the left side was firstly treated and the right side was treated 2 weeks later. Both chest tubes were successfully removed two days after procedures. Although the patient finally died of brain metastases 1 month after pleurodesis, he never recurred pneumothorax. Conclusions: Intrapleural minocycline pleurodesis may be one of the useful and efficacious options in terms of treating intractable pneumothorax associated with SPE. Intrapleural minocycline pleurodesis could be a consideration for intractable pneumothorax related to SPE.

The Association of Asthma and Metabolic Dysfunction With Outcomes of Hospitalized Patients With COVID-19.

BACKGROUND: There have been conflicting results on the association of asthma with the severity of coronavirus disease 2019 (COVID-19). Poor metabolic health has been previously associated with both severe COVID-19 and inflammation in asthma. OBJECTIVES: To examine the association between asthma and COVID-19 outcomes and whether these associations are modified by metabolic syndrome. METHODS: We performed an international, observational cohort study of adult patients hospitalized for COVID-19 from February 2020 through October 2021. The primary outcome was hospital mortality. RESULTS: The study included 27,660 patients from 164 hospitals, 12,114 (44%) female, with a median (interquartile range) age of 63 years (51-75). After adjusting for age, sex, smoking, race, ethnicity, geographic region, and Elixhauser comorbidity index, we found that patients with asthma were not at greater risk of hospital death when compared with patients with no chronic pulmonary disease (controls) (adjusted odds ratio [aOR], 0.97; 95% CI, 0.90-1.04; P = .40). Patients with asthma, when compared with controls, required higher respiratory support identified by the need for supplemental oxygen (aOR, 1.07; 95% CI, 1.01-1.14; P = .02), high-flow nasal cannula or noninvasive mechanical ventilation (aOR, 1.06; 95% CI, 1.00-1.13; P = .04), and invasive mechanical ventilation (aOR, 1.09; 95% CI, 1.03-1.16; P = .003). Metabolic syndrome increased the risk of death in patients with asthma, but the magnitude of observed association was similar to controls in stratified analysis (interaction P value .24). CONCLUSIONS: In this international cohort of hospitalized COVID-19 patients, asthma was not associated with mortality but was associated with increased need for respiratory support. Although metabolic dysfunction was associated with increased risks in COVID-19, these risks were similar for patients with or without asthma.

Pneumomediastinum and pneumothorax in acute respiratory distress syndrome (ARDS) patients: a narrative review.

Background and Objective: Acute respiratory distress syndrome (ARDS) is a severe, life-threatening medical condition characterized by poor oxygenation due to non-compliant lungs secondary diffuse alveolar damage. Encouragingly, the incidence of ARDS has declined steadily recently, attributed mainly to implementation of keystone guidelines and continuous research efforts. Mechanical ventilation is the cornerstone of supportive care for ARDS patients. This review aims to consolidate the current knowledge on pneumothorax (PNX) and pneumomediastinum (PMD) and to enhance the understanding of the readers. The objectives are to (I) explore the etiology and risk factors of PNX and PMD, (II) discuss the various diagnostic modalities available, (III) evaluate management options, and (IV) recent advancements. Methods: A search of the literature was conducted using PubMed, MEDLINE, and Google Scholar for relevant articles pertaining to PNX and PMD in ARDS population. The clinical presentation, diagnostic and management strategies of PNX, PMD, and ARDS were summarized, and all authors reviewed the selection and decide which studies to include. Key Content and Findings: The adoption of lung-protective ventilation strategies, based on the review of literature from the recent years, shows that it has played a significant role in reducing the occurrence of barotrauma, such as PNX and PMD. However, PNX and PMD remains to be a challenging complication to manage. With a specific focus on PNX and PMD, this review provides valuable insights into effectively managing and understanding these critical complications among ARDS patients. Conclusions: ARDS, with its evolving definition, continues to pose a life-threatening threat. Despite the widespread adoption of lung-protective ventilation strategies, PNX and PMD present persistent challenges in management. Further research is imperative to enhance the risk assessment of ARDS patients prone to developing PNX and PMD and to institute more effective prevention and treatment measures.

ProtekDuo percutaneous ventricular support system-physiology and clinical applications.

The ProtekDuo (LivaNova, London, UK) cannula is a dual-lumen device, typically inserted into the right internal jugular (IJ) vein through a percutaneous approach, with fluoroscopy or ultrasound guidance. When connected to a pump, such as the TandemHeart (LivaNova, London, UK) or CentriMag (Abbott, Pleasanton, CA, USA), it can function as a right ventricular (RV) mechanical circulatory support (MCS). When an oxygenator is also added [veno-pulmonary (V-P)], it can provide extracorporeal membrane oxygenation (ECMO) support. This review aims to provide a comprehensive overview of the device's physiology and clinical applications. In the setting of RV failure (RVF), the ProtekDuo cannula, with its outflow in the main pulmonary artery (PA), can bypass the failing RV, improving pulmonary flow, left atrial (LA) filling pressures, and left ventricular (LV) preload. This can also reduce ventricular interdependence and leftward shift of the interventricular septum that occurs in RVF. In this review, the key sections expand on the use of the ProtekDuo cannula in the management of critically ill patients, specifically, the use of ProtekDuo for RV myocardial infarction (MI) RVF, LV assist device (LVAD) implantation-associated RVF, RVF post-heart transplantation, temporary biventricular MCS as bridge to recovery (ECpella 2.0 or PROpella), biventricular support as bridge to recovery or decision, isolated LV failure, post lung transplantation (LT) care, and other miscellaneous clinical scenarios. ProtekDuo is an important tool in the armory of RVF management. The ProtekDuo system is expected to gain more popularity given its clear advantages such as groin-free approach allowing for mobility, easy percutaneous deployment, compatibility with various pumps and oxygenators, and the versatility to be integrated in numerous configurations. In an era of expanding MCS options, further research is needed to better understand the optimal tool for specific patient subsets.

Early machine learning prediction of hospitalized patients at low risk of respiratory deterioration or mortality in community-acquired pneumonia: Derivation and validation of a multivariable model.

Current prognostic tools for pneumonia predominantly focus on mortality, often neglecting other crucial outcomes such as the need for advanced respiratory support. The objective of this study was to develop and validate a tool that predicts the early risk of non-occurrence of respiratory deterioration or mortality. We conducted a single-center, retrospective cohort study involving hospitalized adult patients with community-acquired pneumonia (CAP) and acute hypoxic respiratory failure from January 2009 to December 2019 (n = 4379). We employed the gradient boosting machine (GBM) learning to create a model that estimates the likelihood of patients requiring advanced respiratory support (high flow nasal cannula [HFNC], non-invasive mechanical ventilation [NIMV], and invasive mechanical ventilation [IMV]) or facing mortality during hospitalization. This model utilized readily available data including demographic, physiologic, and laboratory data, sourced from electronic health records and obtained within the first six hours of admission. Out of the cohort, 890 patients (25.2%) either required advanced respiratory support or died during their hospital stay. Our predictive model displayed superior discrimination and higher sensitivity (cross-validation C-statistic = 0.71; specificity = 0.56; sensitivity = 0.72) compared to the pneumonia severity index (PSI) (C-statistic = 0.65; specificity = 0.91; sensitivity = 0.24; P value < 0.001), while maintaining a negative predictive value (NPV) of approximately 0.85. These data demonstrate that our machine learning model predicted the non-occurrence of respiratory deterioration or mortality among hospitalized CAP patients more accurately than the PSI. The enhanced sensitivity of this model holds potential for reliably excluding low-risk patients from pneumonia clinical trials.

Risk factors for failure of conversion from epidural labor analgesia to cesarean section anesthesia and general anesthesia incidence: an updated meta-analysis.

OBJECTIVES: Ongoing controversies persist regarding risk factors associated with the failure of transition from epidural labor analgesia to cesarean section anesthesia, including the duration of labor analgesia, gestational age, and body mass index (BMI). This study aims to provide an updated analysis of the incidence of conversion from epidural analgesia to general anesthesia, while evaluating and analyzing potential risk factors contributing to the failure of this transition to cesarean section anesthesia. METHODS: We conducted an extensive literature search utilizing databases such as PubMed, Embase, Cochrane Library, Web of Science, China National Knowledge Infrastructure (CNKI), WANGFANG, and the Chinese Biomedical Literature Database (CBM) up to September 30, 2022. The meta-analysis was performed using STATA 15.1 software. The quality of the included studies was assessed using the 11-item quality assessment scale recommended by the Agency for Healthcare Research and Quality (AHRQ). RESULTS: A total of 9,926 studies were initially retrieved, and after rigorous selection, 19 studies were included in the meta-analysis. The overall incidence of conversion from epidural analgesia to general anesthesia was found to be 6% (95% confidence interval [CI]: 5-8%). Our findings indicate that, when compared to patients in the successful conversion group, those in the failure group tended to be younger (weighted mean difference [WMD] = -1.571, 95% CI: -1.116 to -0.975) and taller (WMD = 0.893, 95% CI: 0.018-1.767). Additionally, the failure group exhibited a higher incidence of incomplete block in epidural anesthesia, received a higher dosage of additional epidural administration, experienced a greater rate of emergency cesarean sections, and received anesthesia more frequently from non-obstetric anesthesiologists. However, no statistically significant differences were observed in gestational age, depth of the catheter insertion into the skin, epidural catheter specifics, duration of epidural analgesia, infusion rate of epidural analgesia, primiparity status, cervical dilatation during epidural placement, BMI, or weight. CONCLUSION: Our study found that the incidence of conversion from epidural analgesia to cesarean section under general anesthesia was 6%. Notably, the failure group exhibited a higher rate of incomplete block in epidural anesthesia, a greater incidence of emergency cesarean sections, a more frequent provision of anesthesia by non-obstetric anesthesiologists, a higher dosage of epidural administration, and greater height when compared to the success group. Conversely, women in the failure group were younger in age compared to their counterparts in the success group.

Machine Learning for Automated Classification of Abnormal Lung Sounds Obtained from Public Databases: A Systematic Review.

Pulmonary auscultation is essential for detecting abnormal lung sounds during physical assessments, but its reliability depends on the operator. Machine learning (ML) models offer an alternative by automatically classifying lung sounds. ML models require substantial data, and public databases aim to address this limitation. This systematic review compares characteristics, diagnostic accuracy, concerns, and data sources of existing models in the literature. Papers published from five major databases between 1990 and 2022 were assessed. Quality assessment was accomplished with a modified QUADAS-2 tool. The review encompassed 62 studies utilizing ML models and public-access databases for lung sound classification. Artificial neural networks (ANN) and support vector machines (SVM) were frequently employed in the ML classifiers. The accuracy ranged from 49.43% to 100% for discriminating abnormal sound types and 69.40% to 99.62% for disease class classification. Seventeen public databases were identified, with the ICBHI 2017 database being the most used (66%). The majority of studies exhibited a high risk of bias and concerns related to patient selection and reference standards. Summarizing, ML models can effectively classify abnormal lung sounds using publicly available data sources. Nevertheless, inconsistent reporting and methodologies pose limitations to advancing the field, and therefore, public databases should adhere to standardized recording and labeling procedures.

The Diagnostic Accuracy of Metagenomic Next-Generation Sequencing in Diagnosing Pneumocystis Pneumonia: A Systemic Review and Meta-analysis.

Background: Pneumocystis pneumonia (PCP) is a growing concern as the immunocompromised population expands. Current laboratory approaches are limited. This systematic review aimed to evaluate metagenomic next-generation sequencing (MNGS) tests' performance in detecting PCP. Methods: Five databases were searched through December 19, 2022, to identify original studies comparing MNGS with clinically diagnosed PCP. To assess the accuracy, symmetric hierarchical summary receiver operating characteristic models were used. Results: Eleven observational studies reporting 1442 patients (424 with PCP) were included. Six studies focused exclusively on recipients of biologic immunosuppression (none with HIV-associated immunosuppression). Six were exclusively on bronchoalveolar lavage, while 1 was on blood samples. The sensitivity of MGNS was 0.96 (95% CI, 0.90-0.99), and specificity was 0.96 (95% CI, 0.92-0.98), with negative and positive likelihood ratios of 0.02 (95% CI, 0.01-0.05) and 19.31 (95% CI, 10.26-36.36), respectively. A subgroup analysis of studies exclusively including bronchoalveolar lavage (BAL) and blood samples demonstrated a sensitivity of 0.94 (95% CI, 0.78-0.99) and 0.93 (95% CI, 0.80-0.98) and a specificity of 0.96 (95% CI, 0.88-0.99) and 0.98 (95% CI, 0.76-1.00), respectively. The sensitivity analysis on recipients of biologic immunosuppression showed a sensitivity and specificity of 0.96 (95% CI, 0.90-0.98) and 0.94 (95% CI, 0.84-0.98), respectively. The overall confidence in the estimates was low. Conclusions: Despite the low certainty of evidence, MNGS detects PCP with high sensitivity and specificity. This also applies to recipients of biologic immunosuppression and tests performed exclusively on blood samples without the need for BAL. Further studies are required in individuals with HIV-associated immunosuppression.

Pre-hospital use of direct oral anticoagulants agents is associated with a lower risk of major bleeding events in critically ill patients: A single academic center experience.

BACKGROUND: The last decade has witnessed significant advancements in direct oral anticoagulants (DOACs), transforming the landscape of anticoagulation therapy. With the uptrend in DOACs use, critical care physicians are encountering more patients with pre-hospital DOACs prescription. Safety and real world outcomes-related data on DOACs use in critically ill patients are scarce. OBJECTIVE: We assess the risk of major bleeding (MB) events and patient-centered outcomes with pre-hospital use of direct oral anticoagulant agents (DOACs) compared to warfarin therapy. METHODS: Observational study in a single large academic center from January 1st, 2012, through May 4th, 2018. We included adult critically ill patients with warfarin or one of the DOACs, as active medications at the time of hospital admission. The primary outcome was major bleeding (MB), based on the ISTH criteria RESULTS: 99,481 patients were screened; 558 and 3037 patients were included in the final analysis for the DOAC and warfarin groups, respectively. Multivariable analysis showed that the pre-hospital use of DOACs was associated with lower odds for major bleeding events, GI bleeding, need for endoscopic intervention, hemorrhagic shock, any blood transfusion; but higher odds of intracranial bleeding, as compared to warfarin use. There was no difference in hospital length of stay or ICU-free days. CONCLUSIONS: Pre-hospital use of DOACs among critically ill patients is associated with lower major bleeding events, GI bleeding, need for endoscopic intervention, and blood transfusion but a higher risk for intracranial bleeding.

Gaining consensus on expert rule statements for acute respiratory failure digital twin patient model in intensive care unit using a Delphi method.

Digital twin technology is a virtual depiction of a physical product and has been utilized in many fields. Digital twin patient model in healthcare is a virtual patient that provides opportunities to test the outcomes of various interventions virtually without subjecting an actual patient to possible harm. This can serve as a decision aid in the complex environment of the intensive care unit (ICU). Our objective is to develop consensus among a multidisciplinary expert panel on statements regarding respiratory pathophysiology contributing to respiratory failure in the medical ICU. We convened a panel of 34 international critical care experts. Our group modeled elements of respiratory failure pathophysiology using directed acyclic graphs (DAGs) and derived expert statements describing associated ICU clinical practices. The experts participated in three rounds of modified Delphi to gauge agreement on 78 final questions (13 statements with 6 substatements for each) using a Likert scale. A modified Delphi process achieved agreement for 62 of the final expert rule statements. Statements with the highest degree of agreement included the physiology, and management of airway obstruction decreasing alveolar ventilation and ventilation-perfusion matching. The lowest agreement statements involved the relationship between shock and hypoxemic respiratory failure due to heightened oxygen consumption and dead space. Our study proves the utility of a modified Delphi method to generate consensus to create expert rule statements for further development of a digital twin-patient model with acute respiratory failure. A substantial majority of expert rule statements used in the digital twin design align with expert knowledge of respiratory failure in critically ill patients.

Clinical and Echocardiographic Predictors of Recovery of Moderate-to-severe Sepsis-associated Acute Kidney Injury in Critically Ill Patients.

Background: Acute kidney injury (AKI) is associated with significant short- and long-term morbidity and mortality. In critically ill patients with sepsis, AKI tends to be more severe, more likely to require kidney replacement therapy (KRT), with less chance of recovery. Consequently, critically ill patients with sepsis-associated AKI (SA-AKI) have extended intensive care unit (ICU) stays and higher mortality rates. This study evaluated the predictive value of clinical and transthoracic echocardiographic (TTE) parameters for recovery from moderate-to-severe SA-AKI in critically ill patients. Methods: This single-center historical cohort study was conducted at a tertiary academic medical center. We analyzed the data of all adults (age ≥18 years) admitted to the ICU at Mayo Clinic, Rochester, MN, from June 1, 2018, to December 31, 2020. We included all patients who developed sepsis within the initial 24 h of their ICU stay. Results: We identified 2919 eligible septic patients with available TTE, among which 1431 patients (49%) had moderate-to-severe SA-AKI. The mean age of the patients was 68 ± 15 years, and the male-to-female ratio was 1.3:1. The most common comorbidities were diabetes mellitus and chronic lung and kidney diseases. Clinical predictors associated with SA-AKI non-recovery were the presence of stage III AKI (HR 1.5, 95% CI 1.0-2.1, p = 0.03) and utilization of kidney replacement therapy (KRT) (HR 6.8, 95% CI 3.6-12.4, p = 0.01). On the other hand, higher TAPSE was the only TTE variable associated with SA-AKI recovery (HR 1.1; 95% CI 1.08-1.15; p = 0.01). Conclusion: Our data from a single-center provide new information on the clinical (AKI stage, utilization of KRT, BMI, and peak serum creatinine) and echocardiographic features (TAPSE) associated with improved recovery in SA-AKI. There is a definite knowledge gap in the current literature regarding optimizing recovery in moderate-to-severe SA-AKI. Larger, multi-center studies are required to confirm these findings.

The impact of the COVID-19 pandemic on ICU clinical trials: a description of one research team's experience.

BACKGROUND: Disruptions to clinical trials conducted in the intensive care unit (ICU) due to the severe acute respiratory syndrome coronavirus 2 (SARS-CoV2; coronavirus disease 2019 [COVID-19]) pandemic included fewer new trials activated and more trials stopped. While a number of ongoing, non-COVID-19 clinical trials remained open to enrollment, the direct impact of the pandemic on ICUs instilled chaos in this already challenging environment. The numerous challenges need to be reported so investigators can proactively plan and manage these myriad challenges. Thus, the purpose of this study was to describe the impact of the COVID-19 pandemic on screening and accrual for a non-COVID-19 parent clinical trial enrolling critically ill ICU patients receiving mechanical ventilatory support. METHODS: A descriptive, retrospective design using quantitative data from detailed screening logs and qualitative observations with field notes from a parent clinical trial were used to address the objectives. The primary aims of the two-site parent clinical trial (n = 190) are to test the efficacy of self-administration of sedative therapy by mechanically ventilated ICU patients on anxiety and delirium occurrence. ICUs from two academic medical centers [names removed for blinding] plus a community hospital in Minnesota were screened daily for alert patients (Richmond Agitation Sedation Scale [RASS] - 2 to + 1), following commands, hemodynamically stable with sufficient hand grip strength to depress a push-button device. Screening data were summarized based on the primary reason patients were not enrolled (screen failures, declinations of consent). Descriptive statistics (frequencies, percentages), chi-square, and Fisher's Exact test were used to describe the data and to determine any differences among distributions of screening failures and recruitment declinations during the defined pre-pandemic (August 27, 2018-March 15, 2020, 2976 screened patients) and pandemic timeframes (March 16, 2020-February 28, 2022, 3912 screened patients). Qualitative data from varied sources such as screening logs, institutional email communications, staff field notes, and research team meeting minutes were summarized into themes. RESULTS: Despite significantly fewer screen failures due to hypotension, cognitive impairment/dementia, coma, or chemical paralysis with 938 additional patients on the screening log, more were accrued pre-pandemic (n = 55) than during the pandemic period (n = 45); declination reasons were non-significant. Pandemic burdens experienced by study personnel, ICU care providers, and patients/families were revealed that attributed to decreased accrual. CONCLUSIONS: While the parent clinical trial remained opened, cumulative factors adversely impacted the trial during the pandemic period with fewer patients accrued. The human toll of the pandemic on research staff, clinicians, and patients/family members demands that investigators be proactive in managing these challenges to conduct ICU clinical trials successfully, including careful oversight of human and financial resources. TRIAL REGISTRATION: ClinicalTrials.gov NCT#02,819,141 Registered 29 June 2016.

Development and Validation of an Acute Respiratory Distress Syndrome Prediction Model in Coronavirus Disease 2019: Updated Lung Injury Prediction Score.

OBJECTIVE: To develop and validate an updated lung injury prediction score for coronavirus disease 2019 (COVID-19) (c-LIPS) tailored for predicting acute respiratory distress syndrome (ARDS) in COVID-19. PATIENTS AND METHODS: This was a registry-based cohort study using the Viral Infection and Respiratory Illness Universal Study. Hospitalized adult patients between January 2020 and January 2022 were screened. Patients who qualified for ARDS within the first day of admission were excluded. Development cohort consisted of patients enrolled from participating Mayo Clinic sites. The validation analyses were performed on remaining patients enrolled from more than 120 hospitals in 15 countries. The original lung injury prediction score (LIPS) was calculated and enhanced using reported COVID-19-specific laboratory risk factors, constituting c-LIPS. The main outcome was ARDS development and secondary outcomes included hospital mortality, invasive mechanical ventilation, and progression in WHO ordinal scale. RESULTS: The derivation cohort consisted of 3710 patients, of whom 1041 (28.1%) developed ARDS. The c-LIPS discriminated COVID-19 patients who developed ARDS with an area under the curve (AUC) of 0.79 compared with original LIPS (AUC, 0.74; P<.001) with good calibration accuracy (Hosmer-Lemeshow P=.50). Despite different characteristics of the two cohorts, the c-LIPS's performance was comparable in the validation cohort of 5426 patients (15.9% ARDS), with an AUC of 0.74; and its discriminatory performance was significantly higher than the LIPS (AUC, 0.68; P<.001). The c-LIPS's performance in predicting the requirement for invasive mechanical ventilation in derivation and validation cohorts had an AUC of 0.74 and 0.72, respectively. CONCLUSION: In this large patient sample c-LIPS was successfully tailored to predict ARDS in COVID-19 patients.

Pulmonary Cryptococcosis and Pulmonary Fibrosis: A Complication of COVID-19 Pneumonia.

Acute respiratory distress syndrome (ARDS) and pulmonary fibrosis (PF) are increasingly identified as complications of coronavirus disease 2019 (COVID-19) infection, the latter being managed with tapering dose glucocorticoids. Studies have shown improved outcomes with steroid use in this subset of patients; however, the use of high doses of steroids predisposes these patients to develop various complications such as opportunistic infections. The incidence of pulmonary cryptococcosis (PC) in patients with post-COVID-19 PF is not known. Here, we discuss a middle-aged male, with no pulmonary comorbidities, who developed PC secondary to the immunocompromised state from high-dose steroid use for the management of post-COVID-19 PF.

Developing DELPHI expert consensus rules for a digital twin model of acute stroke care in the neuro critical care unit.

INTRODUCTION: Digital twins, a form of artificial intelligence, are virtual representations of the physical world. In the past 20 years, digital twins have been utilized to track wind turbines' operations, monitor spacecraft's status, and even create a model of the Earth for climate research. While digital twins hold much promise for the neurocritical care unit, the question remains on how to best establish the rules that govern these models. This model will expand on our group's existing digital twin model for the treatment of sepsis. METHODS: The authors of this project collaborated to create a Direct Acyclic Graph (DAG) and an initial series of 20 DELPHI statements, each with six accompanying sub-statements that captured the pathophysiology surrounding the management of acute ischemic strokes in the practice of Neurocritical Care (NCC). Agreement from a panel of 18 experts in the field of NCC was collected through a 7-point Likert scale with consensus defined a-priori by ≥ 80% selection of a 6 ("agree") or 7 ("strongly agree"). The endpoint of the study was defined as the completion of three separate rounds of DELPHI consensus. DELPHI statements that had met consensus would not be included in subsequent rounds of DELPHI consensus. The authors refined DELPHI statements that did not reach consensus with the guidance of de-identified expert comments for subsequent rounds of DELPHI. All DELPHI statements that reached consensus by the end of three rounds of DELPHI consensus would go on to be used to inform the construction of the digital twin model. RESULTS: After the completion of three rounds of DELPHI, 93 (77.5%) statements reached consensus, 11 (9.2%) statements were excluded, and 16 (13.3%) statements did not reach a consensus of the original 120 DELPHI statements. CONCLUSION: This descriptive study demonstrates the use of the DELPHI process to generate consensus among experts and establish a set of rules for the development of a digital twin model for use in the neurologic ICU. Compared to associative models of AI, which develop rules based on finding associations in datasets, digital twin AI created by the DELPHI process are easily interpretable models based on a current understanding of underlying physiology.

Septic shock definitions and associated outcomes in blood culture positive critically ill patients.

Background: The proposed definition of septic shock in the Sepsis-3 consensus statement has been previously validated in critically ill patients. However, the subset of critically ill patients with sepsis and positive blood cultures needs further evaluation. To compare the combined (old and new septic shock) versus old definition of septic shock in sepsis patients that have positive blood cultures and are critically ill. Methods: A retrospective cohort study of adult patients (age ≥18 years), who had evidence of positive blood cultures, requiring intensive care unit (ICU) admission at a large tertiary care academic center from January 2009 through October 2015. Eligible subjects who opted out of research participation, those requiring intensive care admission after elective surgery, and those who were deemed to have a low probability of infection were excluded. Basic demographics data, clinical and laboratory parameters, and outcomes of interest were pulled from the validated institutional database/repository and contrasted between the patients who qualified the new and old definitions criteria (combined) of septic shock versus the group meeting the old septic shock criteria only. Results: We included a total of 477 patients in the final analysis who qualified for old and new septic shock definitions. For the entire cohort, median age was 65.6 (IQR, 55-75) years, with male predominance (N=258, 54%). When compared to patients in the group who only met the old definition (N=206), the patients who met the combined (new or both new and old, N=271) definition had a higher APACHE III scores, 92 (IQR, 76-112) vs. 76 (IQR, 61-95), P<0.001; a higher SOFA day-1 score of 10 (IQR, 8-13) vs. 7 (IQR, 4-10), P<0.001, but did not differ significantly in age 65.5 years (IQR, 55-74) vs. 66 years (IQR, 55-76) years, P=0.47. The patients who met the combined (new or both new and old) definition had higher chances of having conservative resuscitation preferences (DNI/DNR); 77 (28.4) vs. 22 (10.7), P<0.001. The same group also had worse outcomes in terms of hospital mortality (34.3% vs. 18%, P<0.001) and standardized mortality ratio (0.76 vs. 0.52, P<0.04). Conclusions: In patients with sepsis with positive blood cultures, the group of patients meeting the combined definition (new or both new and old) have higher severity of illness, higher mortality, and a worse standardized mortality ratio as compared to patients meeting the old definition of septic shock.

CHARACTERISTICS AND PREDICTORS OF PATIENTS WITH SEPSIS WHO ARE CANDIDATES FOR MINIMALLY INVASIVE APPROACH OUTSIDE OF INTENSIVE CARE UNIT.

ABSTRACT: Objective: To identify and describe characteristics of patients with sepsis who could be treated with minimally invasive sepsis (MIS) approach without intensive care unit (ICU) admission and to develop a prediction model to select candidates for MIS approach. Methods: A secondary analysis of the electronic database of patients with sepsis at Mayo Clinic, Rochester, MN. Candidates for the MIS approach were adults with septic shock and less than 48 hours of ICU stay, who did not require advanced respiratory support and were alive at hospital discharge. Comparison group consisted of septic shock patients with an ICU stay of more than 48 hours without advanced respiratory support at the time of ICU admission. Results: Of 1795 medical ICU admissions, 106 patients (6%) met MIS approach criteria. Predictive variables (age >65 years, oxygen flow >4 L/min, temperature <37°C, creatinine >1.6 mg/dL, lactate >3 mmol/L, white blood cells >15 × 10 9 /L, heart rate >100 beats/min, and respiration rate >25 breaths/min) selected through logistic regression were translated into an 8-point score. Model discrimination yielded the area under the receiver operating characteristic curve of 79% and was well fitted (Hosmer-Lemeshow P = 0.94) and calibrated. The MIS score cutoff of 3 resulted in a model odds ratio of 0.15 (95% confidence interval, 0.08-0.28) and a negative predictive value of 91% (95% confidence interval, 88.69-92.92). Conclusions: This study identifies a subset of low-risk septic shock patients who can potentially be managed outside the ICU. Once validated in an independent, prospective sample our prediction model can be used to identify candidates for MIS approach.

Acute on chronic liver failure: prognostic models and artificial intelligence applications.

Critically ill patients presenting with acute on chronic liver failure (ACLF) represent a particularly vulnerable population due to various considerations surrounding the syndrome definition, lack of robust prospective evaluation of outcomes, and allocation of resources such as organs for transplantation. Ninety-day mortality related to ACLF is high and patients who do leave the hospital are frequently readmitted. Artificial intelligence (AI), which encompasses various classical and modern machine learning techniques, natural language processing, and other methods of predictive, prognostic, probabilistic, and simulation modeling, has emerged as an effective tool in various areas of healthcare. These methods are now being leveraged to potentially minimize physician and provider cognitive load and impact both short-term and long-term patient outcomes. However, the enthusiasm is tempered by ethical considerations and a current lack of proven benefits. In addition to prognostic applications, AI models can likely help improve the understanding of various mechanisms of morbidity and mortality in ACLF. Their overall impact on patient-centered outcomes and countless other aspects of patient care remains unclear. In this review, we discuss various AI approaches being utilized in healthcare and discuss the recent and expected future impact of AI on patients with ACLF through prognostic modeling and AI-based approaches.