Citations

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.

AbstractBackgroundPneumocystis 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.MethodsFive 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.ResultsEleven 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.ConclusionsDespite 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.

Abstract: The COVID-19 pandemic, caused by the coronavirus, SARS-CoV-2, has claimed millions of lives worldwide in the past two years. Fatalities among the elderly with underlying cardiovascular disease, lung disease, and diabetes have particularly been high. A biometrics analysis on author’s keywords was carried out, and searched for possible links between various coronavirus studies over the past 50 years, and integrated them. We found keywords like immune system, immunity, nutrition, malnutrition, micronutrients, exercise, inflammation, and hyperinflammation were highly related to each other. Based on these findings, we hypothesized that the human immune system is a multilevel super complex system, which employs multiple strategies to contain microorganism infections and restore homeostasis. It was also found that the behavior of the immune system is not able to be described by a single immunological theory. However, one main strategy is “self-destroy and rebuild”, which consists of a series of inflammatory responses: 1) active self-destruction of damaged/dysfunctional somatic cells; 2) removal of debris and cells; 3) rebuilding tissues. Thus, invading microorganisms’ clearance could be only a passive bystander response to this destroy–rebuild process. Microbial infections could be self-limiting and promoted as an indispensable essential nutrition for the vast number of genes existing in the microorganisms. The transient nutrition surge resulting from the degradation of the self-destroyed cell debris coupled with the existing nutrition state in the patient may play an important role in the pathogenesis of COVID-19. Finally, a possible coping strategies to mitigate COVID-19, including vaccination is also discussed.

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.

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 2,919 eligible septic patients with available TTE, among which 1,431 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.