Can vital signs recorded in patients' homes aid decision making in emergency care? A Scoping Review

Aim: Use of tele-health programs and wearable sensors that allow patients to monitor their own vital signs have been expanded in response to COVID-19. We aimed to explore the utility of patient-held data during presentation as medical emergencies. Methods: We undertook a systematic scoping review of two groups of studies: studies using non-invasive vital sign monitoring in patients with chronic diseases aimed at preventing unscheduled reviews in primary care, hospitalization or emergency department visits and studies using vital sign measurements from wearable sensors for decision making by clinicians on presentation of these patients as emergencies. Only studies that described a comparator or control group were included. Studies limited to inpatient use of devices were excluded. Results: The initial search resulted in 896 references for screening, nine more studies were identified through searches of references. 26 studies fulfilled inclusion and exclusion criteria and were further analyzed. The majority of studies were from telehealth programs of patients with congestive heart failure or Chronic Obstructive Pulmonary Disease. There was limited evidence that patient held data is currently used to risk-stratify the admission or discharge process for medical emergencies. Studies that showed impact on mortality or hospital admission rates measured vital signs at least daily. We identified no interventional study using commercially available sensors in watches or smart phones. Conclusions: Further research is needed to determine utility of patient held monitoring devices to guide management of acute medical emergencies at the patients’ home, on presentation to hospital and after discharge back to the community

Artificial Intelligence in Sepsis

Healthcare today generates large amounts of data. We can use technologies such as artificial intelligence (AI) to gain valuable new insights into complex problems. Over the past few years, the sepsis research field has shown considerable interest in using AI applications to improve patient care. This chapter provides a high-level overview of AI and the various techniques prevalent in the AI literature for sepsis. With that baseline knowledge of AI techniques, we will look at studies examining AI assistance to understand the sepsis pathogenesis or improve the sepsis diagnosis, treatment, and prognosis. We will conclude the chapter with a discussion of challenges that we still need to overcome to mainstream AI in sepsis management

Homocysteine and asymmetric dimethylarginine (ADMA): Biochemically linked but differently related to vascular disease in chronic kidney disease

Asymmetric dimethylarginine (ADMA), an endogenous inhibitor of nitric oxide synthase, is formed by methylation of arginine residues in proteins and released after proteolysis. In this reaction, S-adenosylmethionine is methyldonor and S-adenosylhomocysteine the demethylated product. ADMA and homocysteine are thus biochemically linked. Both plasma homocysteine and ADMA concentrations are increased in patients with renal dysfunction, probably as a result of an impairment in their metabolic, but not urinary, clearance. Hyperhomocysteinemia has been associated with an increased risk of cardiovascular disease in end-stage renal disease, especially in patients without malnutrition and inflammation. Also, plasma ADMA levels have been associated with cardiovascular disease in renal failure patients. Both homocysteine and ADMA are thought to mediate their adverse vascular effects by impairing endothelial, nitric oxide-dependent function resulting in decreased vasodilatation, increased smooth muscle cell proliferation, platelet dysfunction and increased monocyte adhesion. At the same time, it has been shown that the correlation between plasma ADMA and homocysteine is weak and that, in renal patients, the association of plasma ADMA carotid intima-media thickness, cardiovascular events and overall mortality is independent of homocysteine. This indicates that the negative vascular effects of ADMA and homocysteine have a different etiology. Treatment with folic acid substantially lowers homocysteine, but not ADMA concentration. So far, homocysteine-lowering therapy has not been very successful in decreasing cardiovascular disease. In patients with renal failure, ADMA reduction may be an interesting new goal in the prevention of cardiovascular disease

Timeliness of antibiotics for patients with sepsis and septic shock

For many years, sepsis guidelines have focused on early administration of antibiotics. While this practice may benefit some patients, for others it might have detrimental consequences. The increasingly shortened timeframes in which administration of antibiotics is recommended, have forced physicians to sacrifice diagnostic accuracy for speed, encouraging the overuse of antibiotics. The evidence supporting this practice is based on retrospective data, with all the limitations attached, while the only randomized trial on this subject does not show a mortality benefit from early administration of antibiotics in a population of patients with sepsis as often seen in the emergency department (ED). Physicians are challenged to treat patients suspected of having sepsis within a short period of time, while the real challenge should be to identify patients who would not be harmed by withholding treatment with antibiotics until the diagnosis of infection with a bacterial origin is confirmed and the appropriateness of a course of antibiotics can be evaluated more adequately. Therefore, in the general population of patients with sepsis, taking the time to gather additional data to confirm the diagnosis should be encouraged without a specific timeframe, although physicians should be encouraged to perform an adequate work-up as soon as possible. Patients with suspected sepsis and signs of shock should immediately be treated with antibiotics, as there is no margin for error

Artificial Intelligence for the Prediction of In-Hospital Clinical Deterioration: A Systematic Review

OBJECTIVES: To analyze the available literature on the performance of artificial intelligence-generated clinical models for the prediction of serious life-threatening events in non-ICU adult patients and evaluate their potential clinical usage. DATA SOURCES: The PubMed database was searched for relevant articles in English literature from January 1, 2000, to January 23, 2022. Search terms, including artificial intelligence, machine learning, deep learning, and deterioration, were both controlled terms and free-text terms. STUDY SELECTION: We performed a systematic search reporting studies that showed performance of artificial intelligence-based models with outcome mortality and clinical deterioration. DATA EXTRACTION: Two review authors independently performed study selection and data extraction. Studies with the same outcome were grouped, namely mortality and various forms of deterioration (including ICU admission, adverse events, and cardiac arrests). Meta-analysis was planned in case sufficient data would be extracted from each study and no considerable heterogeneity between studies was present. DATA SYNTHESIS: In total, 45 articles were included for analysis, in which multiple methods of artificial intelligence were used. Twenty-four articles described models for the prediction of mortality and 21 for clinical deterioration. Due to heterogeneity of study characteristics (patient cohort, outcomes, and prediction models), meta-analysis could not be performed. The main reported measure of performance was the area under the receiver operating characteristic (AUROC) (n = 38), of which 33 (87%) had an AUROC greater than 0.8. The highest reported performance in a model predicting mortality had an AUROC of 0.935 and an area under the precision-recall curve of 0.96. CONCLUSIONS: Currently, a growing number of studies develop and analyzes artificial intelligence-based prediction models to predict critical illness and deterioration. We show that artificial intelligence-based prediction models have an overall good performance in predicting deterioration of patients. However, external validation of existing models and its performance in a clinical setting is highly recommended

Embracing cohort heterogeneity in clinical machine learning development: a step toward generalizable models

Abstract This study is a simple illustration of the benefit of averaging over cohorts, rather than developing a prediction model from a single cohort. We show that models trained on data from multiple cohorts can perform significantly better in new settings than models based on the same amount of training data but from just a single cohort. Although this concept seems simple and obvious, no current prediction model development guidelines recommend such an approach

An overview of positive cultures and clinical outcomes in septic patients: a sub-analysis of the Prehospital Antibiotics Against Sepsis (PHANTASi) trial

Background: Sepsis remains one of the most important causes of morbidity and mortality worldwide. In approximately 30-50% of cases of suspected sepsis, no pathogen is isolated, disabling the clinician to treat the patient with targeted antimicrobial therapy. Studies investigating the differences in the patient outcomes between culture-positive and culture-negative sepsis patients have only been conducted in subgroups of sepsis patients and results are ambiguous. Methods: This is a sub-analysis of the PHANTASi (Prehospital Antibiotics Against Sepsis trial), a randomized controlled trial that focused on the effect of prehospital antibiotics in sepsis patients. We evaluated the outcome of cultures from different sources and determined what the clinical implications of having a positive culture compared to negative cultures were for patient outcomes. Furthermore, we looked at the effect of antibiotics on culture outcomes. Results: 1133 patients (42.6%) with culture-positive sepsis were identified, compared to 1526 (56.4%) patients with culture-negative sepsis. 28-day mortality (RR 1.43 [95% CI 1.11-1.83]) and 90-day mortality (RR 1.41 [95% CI 1.15-1.71]) were significantly higher in culture-positive patients compared to culture-negative patients. Culture-positive sepsis was also associated with ≥ 3 organ systems affected during the sepsis episode (RR 4.27 [95% CI 2.78-6.60]). Patients who received antibiotics at home more often had negative blood cultures (85.9% vs. 78%) than those who did not (p < 0.001). Conclusions: Our results show that culture-positive sepsis is associated with a higher mortality rate and culture-positive patients more often have multiple organ systems affected during the sepsis episode