An explainable machine learning-based model to predict intensive care unit admission among patients with community-acquired pneumonia and connective tissue disease.

BACKGROUND: There is no individualized prediction model for intensive care unit (ICU) admission on patients with community-acquired pneumonia (CAP) and connective tissue disease (CTD) so far. In this study, we aimed to establish a machine learning-based model for predicting the need for ICU admission among those patients. METHODS: This was a retrospective study on patients admitted into a University Hospital in China between November 2008 and November 2021. Patients were included if they were diagnosed with CAP and CTD during admission and hospitalization. Data related to demographics, CTD types, comorbidities, vital signs and laboratory results during the first 24 h of hospitalization were collected. The baseline variables were screened to identify potential predictors via three methods, including univariate analysis, least absolute shrinkage and selection operator (Lasso) regression and Boruta algorithm. Nine supervised machine learning algorithms were used to build prediction models. We evaluated the performances of differentiation, calibration, and clinical utility of all models to determine the optimal model. The Shapley Additive Explanations (SHAP) and Local Interpretable Model-Agnostic Explanations (LIME) techniques were performed to interpret the optimal model. RESULTS: The included patients were randomly divided into the training set (1070 patients) and the testing set (459 patients) at a ratio of 70:30. The intersection results of three feature selection approaches yielded 16 predictors. The eXtreme gradient boosting (XGBoost) model achieved the highest area under the receiver operating characteristic curve (AUC) (0.941) and accuracy (0.913) among various models. The calibration curve and decision curve analysis (DCA) both suggested that the XGBoost model outperformed other models. The SHAP summary plots illustrated the top 6 features with the greatest importance, including higher N-terminal pro-B-type natriuretic peptide (NT-proBNP) and C-reactive protein (CRP), lower level of CD4 + T cell, lymphocyte and serum sodium, and positive serum (1,3)-ß-D-glucan test (G test). CONCLUSION: We successfully developed, evaluated and explained a machine learning-based model for predicting ICU admission in patients with CAP and CTD. The XGBoost model could be clinical referenced after external validation and improvement.

Effect of obstructive sleep apnea on postoperative delirium: a system review and meta-analysis.

PURPOSE: To investigate the impact of obstructive sleep apnea (OSA) on postoperative delirium (PD), and evaluate the effectiveness of positive airway pressure (PAP) therapy on PD among OSA patients. METHODS: We systematically searched Embase, Cochrane Library and PubMed databases from their establishment to November 27, 2022. A random-effects approach was employed to determine aggregated results. Subgroup and sensitivity analyses were carried out to investigate heterogeneity. RESULTS: Sixteen eligible studies were included in the analysis. Thirteen studies revealed that OSA significantly elevated the likelihood of developing PD (OR = 1.71; 95%CI = 1.17 to 2.49; p = 0.005). Subgroup analysis according to delirium assessment scales showed that OSA did not exhibit an association with the incidence of PD assessed by the Confusion Assessment Method-Intensive Care Unit (OR = 1.14; 95%CI = 0.77 to 1.67; p = 0.51) but enhanced the likelihood of developing PD evaluated with other measurement scales (OR = 2.15; 95%CI = 1.44 to 3.19; p = 0.0002). Three additional studies explored the impact of PAP treatment on PD among OSA individuals, indicating no significant reduction in PD incidence with PAP use (OR = 0.58; 95%CI = 0.13 to 2.47; p = 0.46). CONCLUSIONS: OSA may not be a risk factor for PD in critically ill patients in the intensive care unit, but may increase the likelihood of developing PD among individuals receiving regular care in the ward postoperatively. The efficacy of PAP therapy in decreasing PD incidence among OSA patients remains debatable.

Clinical profile analysis and nomogram for predicting in-hospital mortality among elderly severe community-acquired pneumonia patients: a retrospective cohort study.

BACKGROUND: Severe community-acquired pneumonia is one of the most lethal forms of CAP with high mortality. For rapid and accurate decisions, we developed a mortality prediction model specifically tailored for elderly SCAP patients. METHODS: The retrospective study included 2365 elderly patients. To construct and validate the nomogram, we randomly divided the patients into training and testing cohorts in a 70% versus 30% ratio. The primary outcome was in-hospital mortality. Univariate and multivariate logistic regression analyses were used in the training cohort to identify independent risk factors. The robustness of this model was assessed using the C index, ROC and AUC. DCA was employed to evaluate the predictive accuracy of the model. RESULTS: Six factors were used as independent risk factors for in-hospital mortality to construct the prediction model, including age, the use of vasopressor, chronic renal disease, neutrophil, platelet, and BUN. The C index was 0.743 (95% CI 0.719-0.768) in the training cohort and 0.731 (95% CI 0.694-0.768) in the testing cohort. The ROC curves and AUC for the training cohort and testing cohort (AUC = 0.742 vs. 0.728) indicated a robust discrimination. And the calibration plots showed a consistency between the prediction model probabilities and observed probabilities. Then, the DCA demonstrated great clinical practicality. CONCLUSIONS: The nomogram incorporated six risk factors, including age, the use of vasopressor, chronic renal disease, neutrophil, platelet and BUN, which had great predictive accuracy and robustness, while also demonstrating clinical practicality at ICU admission.

The prognostic value of C-reactive protein to albumin ratio in patients with sepsis: a systematic review and meta-analysis.

OBJECTIVE: This study aimed to determine whether the C-reactive protein-to-albumin ratio (CAR) can serve as a prognostic marker in patients with sepsis. METHODS: Chinese and English databases were searched to retrieve the included literature. The pooled sensitivity (SEN), specificity (SPE), positive likelihood ratio (PLR), negative likelihood ratio (NLR), diagnostic odds ratio (DOR), and area under the curve (AUC) of the summary receiver operating characteristic (SROC) with their 95% confidence interval (CI) were calculated using the bivariate model. Moreover, the hazard ratio (HR) and 95% CI were calculated using the random effect model. RESULTS: Nine articles comprising 3224 patients with sepsis were included in the meta-analysis. The pooled SEN was 0.73 (95% CI 0.65-0.80), the pooled SPE was 0.78 (95% CI 0.69-0.84), the pooled PLR was 3.29 (95% CI 2.15-5.03), the pooled NLR was 0.35 (95% CI 0.24-0.49), and the pooled DOR was 9.50 (95% CI 4.38-20.59). The AUC under the SROC was 0.82 (95% CI 0.78-0.85) for the prognostic meta-analysis. The pooled HR was 1.10 (95% CI 1.02-1.18). CONCLUSIONS: This meta-analysis suggests that a high CAR level is associated with increased mortality and a poor prognosis.

Genetic Association of Circulating Adipokines with Risk of Idiopathic Pulmonary Fibrosis: A Two-Sample Mendelian Randomization Study.

PURPOSE: The causal relationships between circulating adipokines and idiopathic pulmonary fibrosis (IPF) are yet to be established. We performed a two-sample Mendelian randomization (MR) study to investigate the causal roles of adipokines on IPF risk. METHODS: We analyzed the summary data from genome-wide association studies (GWAS), including adiponectin, leptin, resistin and monocyte chemoattractant protein-1 (MCP-1) and IPF. The inverse-variance weighted (IVW) method was considered as the major method and the MR-Egger, weighted median, simple mode and weighted mode were utilized as complementary methods. We also performed the sensitivity analyses, including heterogeneity test, horizontal pleiotropy test and leave-one-out analysis. RESULTS: The selected number of single nucleotide polymorphisms (SNPs) was 13 for adiponectin, 6 for leptin,12 for resistin, and 6 for MCP-1, respectively. The results showed a causal effect of the circulating adiponectin levels on the risk of IPF (OR 0.645, 95% CI 0.457-0.911, P = 0.013). However, we did not observe significant associations of genetic changes in serum leptin (OR 1.018, 95% CI 0.442-2.346, P = 0.967), resistin (OR 1.002, 95% CI 0.712-1.408, P = 0.993), and MCP-1 (OR 1.358, 95% CI 0.891-2.068, P = 0.155) with risk of developing IPF. There was no evidence of heterogeneity or horizontal pleiotropy. The sensitivity analyses confirmed that our results were stable and reliable. CONCLUSIONS: The increase in serum adiponectin was associated causally with a decreased risk of developing IPF. There is no evidence to support a causal association between leptin, resistin or MCP-1 with risk of IPF. Further studies are needed to confirm our findings.

Association of admission lactate with mortality in adult patients with severe community-acquired pneumonia.

PURPOSE: The present study was conducted to investigate the association of admission lactate with mortality in severe community-acquired pneumonia (SCAP). METHODS: We performed a retrospective, observational, cohort study on adult SCAP patients admitted to intensive care unit (ICU) in West China Hospital of Sichuan University between December 2011 and December 2018. The primary outcome was hospital mortality. Univariate and then multivariate analysis were performed to identify independent risk factors for hospital mortality. The association of admission lactate categories with hospital mortality was examined in three logistic regression models and Kaplan-Meier plots. We also applied restricted cubic splines to estimate the potential non-linear associations. RESULTS: In total, 2275 SCAP patients were included. Admission lactate remained a significant factor for mortality after multivariate regression (OR: 1.085; 95% CI: 1.033,1.141; by continuous variable). After lactate was categorized into quartiles and the confounders were fully adjusted, compared with the quartile 1, ORs (95% CIs) of hospital mortality for quartile 2, quartile 3 and quartile 4 were 1.001 (0.759-1.321), 1.153 (0.877-1.516) and 1.593 (1.202-2.109), respectively (P for trend =0.001). Survival curves indicated that elevated lactate was associated with poor prognosis (P < 0.001). Moreover, this association was non-linear, indicating that increased lactate has the most notable impact on mortality within the range of 1.5 to 4 mmol/L (P non-linear: 0.029 for hospital mortality; 0.004 for ICU mortality). CONCLUSION: Elevated admission lactate has a significant, independent, and potentially non-linear association with increased mortality in SCAP patients.

Modifiable risk factors for ventilator associated diaphragmatic dysfunction: a multicenter observational study.

BACKGROUND: Diaphragmatic dysfunction is known to be associated with difficulties weaning from invasive mechanical ventilation and is related to worse patient outcomes yet our understanding of how to prevent diaphragmatic dysfunction remains incomplete. We examined potentially modifiable risk factors for diaphragmatic dysfunction and attempted to estimate benefits attributable to altering these modifiable risk factors. METHODS: This prospective multicenter observational study was undertaken in the general ICUs of two tertiary care teaching hospitals. Critically ill adults expected to receive invasive mechanical ventilation for at least 48 h were enrolled. Diaphragm function was assessed by ultrasound each study day, with dysfunction defined as thickening fraction less than 20%. RESULTS: From January to December 2019, 856 patients were screened and 126 patients were enrolled. Overall, 40.5% (51/126) of patients experienced diaphragmatic dysfunction during invasive mechanical ventilation. Patients with diaphragmatic dysfunction were more likely to develop ventilator associated pneumonia (risk difference [RD] + 12.9%, 95% Confidence Interval [CI] 1.4 to 24.4%, P = 0.028), were more likely to experience extubation failure (RD + 8.5%, 95% CI 0.4 to 16.6%, P = 0.039) and required a longer duration of invasive mechanical ventilation (RD + 1.3 days, 95% CI 0.1 to 2.5 days, P = 0.035). They also required a longer hospital stay (RD + 1.2 days, 95% CI 0.04 to 2.4 days, P = 0.041) and were more likely to die before hospital discharge (RD + 18.1%, 95% CI 3.7 to 32.5%, P = 0.014). Multivariable analysis considered the impact of age, sex, pre-existing nutritional status, caloric intake, amino acid intake, acute disease severity, modes of mechanical ventilation, measures of respiratory status, sedation, pain control and baseline diaphragm thickness. Only SOFA score (P = 0.008) and early amino acid intake (P = 0.001) remained significant independent risk factors for the onset of diaphragmatic dysfunction. Causal path modeling suggested early amino acid intake may significantly reduce diaphragmatic dysfunction (RRR 29%, 95% CI 10% to 48%, P = 0.003) and may also reduce mortality (RRR 49%, 95% CI 25% to 73%, P < 0.0001). CONCLUSIONS: Amino acid intake during the first 24 h of ICU stay may represent an important, modifiable risk factor for diaphragmatic dysfunction and may have a direct causal effect on mortality. We recommend additional research on this topic.

A prediction model for hospital mortality in patients with severe community-acquired pneumonia and chronic obstructive pulmonary disease.

BACKGROUND: No personalized prediction model or standardized algorithm exists to identify those at high risk of death among severe community-acquired pneumonia (SCAP) patients with chronic obstructive pulmonary disease (COPD). The aim of this study was to investigate the risk factors and to develop a useful nomogram for prediction of mortality in those patients. METHODS: We performed a retrospective, observational, cohort study in the intensive care unit (ICU) of West China Hospital, Sichuan University with all consecutive SCAP patients with COPD between December 2011 and December 2018. The clinical data within 24 h of admission to ICU were collected. The primary outcome was hospital mortality. We divided the patients into training and testing cohorts (70% versus 30%) randomly. In the training cohort, univariate and multivariate logistic regression analysis were used to identify independent risk factors applied to develop a nomogram. The prediction model was assessed in both training and testing cohorts. RESULTS: Finally, 873 SCAP patients with COPD were included, among which the hospital mortality was 41.4%. In training cohort, the independent risk factors for hospital mortality were increased age, diabetes, chronic renal diseases, decreased systolic blood pressure (SBP), and elevated fibrinogen, interleukin 6 (IL-6) and blood urea nitrogen (BUN). The C index was 0.840 (95% CI 0.809-0.872) in training cohort and 0.830 (95% CI 0.781-0.878) in testing cohort. Furthermore, the time-dependent AUC, calibration plots, DCA and clinical impact curves indicated the model had good predictive performance. Significant association of risk stratification based on nomogram with mortality was also found (P for trend < 0.001). The restricted cubic splines suggested that estimated associations between these predictors and hospital mortality were all linear relationships. CONCLUSION: We developed a prediction model including seven risk factors for hospital mortality in patients with SCAP and COPD. It can be used for early risk stratification in clinical practice after more external validation.

Clinical profile analysis and nomogram for predicting in-hospital mortality among elderly severe community-acquired pneumonia patients with comorbid cardiovascular disease: a retrospective cohort study.

BACKGROUND: Researchers have linked cardiovascular disease (CVD) with advancing age; however, how it drives disease progression in elderly severe community acquired pneumonia (SCAP) patients is still unclear. This study aims to identify leading risk predictors of in-hospital mortality in elderly SCAP patients with CVD, and construct a comprehensive nomogram for providing personalized prediction. PATIENTS AND METHODS: The study retrospectively enrolled 2365 elderly patients identified SCAP. Among them, 413 patients were found to have CVD. The LASSO regression and multivariate logistic regression analysis were utilized to select potential predictors of in-hospital mortality in elderly SCAP patients with CVD. By incorporating these features, a nomogram was then developed and subjected to internal validations. Discrimination, calibration, and clinical use of the nomogram were assessed via C-index, calibration curve analysis, and decision plot. RESULTS: Compared with patients without CVD, elderly SCAP patients with CVD had a significant poor outcome. Further analysis of the CVD population identified 7 independent risk factors for in-hospital mortality in elderly SCAP patients, including age, the use of vasopressor, numbers of primary symptoms, body temperature, monocyte, CRP and NLR. The nomogram model incorporated these 7 predictors showed sufficient predictive accuracy, with the C-index of 0.800 (95% CI 0.758-0.842). High C-index value of 0.781 was obtained in the internal validation via bootstrapping validation. Moreover, the calibration curve indicative a good consistency of risk prediction, and the decision curve manifested that the nomogram had good overall net benefits. CONCLUSION: An integrated nomogram was developed to facilitate the personalized prediction of in-hospital mortality in elderly SCAP patients with CVD.

A prediction model for major adverse cardiovascular events (MACE) in patients with coronavirus disease 2019 (COVID-19).

BACKGROUND: Emerging evidence shows that cardiovascular injuries and events in coronavirus disease 2019 (COVID-19) should be considered. The current study was conducted to develop an early prediction model for major adverse cardiovascular events (MACE) during hospitalizations of COVID-19 patients. METHODS: This was a retrospective, multicenter, observational study. Hospitalized COVID-19 patients from Wuhan city, Hubei Province and Sichuan Province, China, between January 14 and March 9, 2020, were randomly divided into a training set (70% of patients) and a testing set (30%). All baseline data were recorded at admission or within 24 h after admission to hospitals. The primary outcome was MACE during hospitalization, including nonfatal myocardial infarction, nonfatal stroke and cardiovascular death. The risk factors were selected by LASSO regression and multivariate logistic regression analysis. The nomogram was assessed by calibration curve and decision curve analysis (DCA). RESULTS: Ultimately, 1206 adult COVID-19 patients were included. In the training set, 48 (5.7%) patients eventually developed MACE. Six factors associated with MACE were included in the nomogram: age, PaO2/FiO2 under 300, unconsciousness, lymphocyte counts, neutrophil counts and blood urea nitrogen. The C indices were 0.93 (95% CI 0.90, 0.97) in the training set and 0.81 (95% CI 0.70, 0.93) in the testing set. The calibration curve and DCA demonstrated the good performance of the nomogram. CONCLUSIONS: We developed and validated a nomogram to predict the development of MACE in hospitalized COVID-19 patients. More prospective multicenter studies are needed to confirm our results.

Neuromedin U: potential roles in immunity and inflammation.

Since the discovery of neuromedin U (NmU) from porcine spinal cord in 1985, this neuropeptide has been subsequently identified in many other species with multiple physiological and pathophysiological roles detected, ranging from smooth muscle contraction, feeding, energy balance to tumorigenesis. Intriguingly, NmU is also emerging to play pro-inflammatory roles involving immune cell activation and cytokine release in a neuron-dependent or neuron-independent manner. The NmU-mediated inflammatory responses have already been observed in worm infection, sepsis, autoimmune arthritis and allergic animal models. In this review, we focus on the roles of NmU in immunity and inflammation by highlighting the interactions between NmU and immune cells, summarizing the signalling mechanism involved in their reactions and discussing its potential contributions to inflammatory diseases.

Efficacy and safety of umifenovir for coronavirus disease 2019 (COVID-19): A systematic review and meta-analysis.

We conducted this systemic review and meta-analysis in an attempt to evaluate the efficacy and safety of umifenovir in coronavirus disease 2019 (COVID-19). We searched PubMed, Web of Science, Embase, Cochrane Library, China National Knowledge Infrastructure, and medRxiv database. We included both retrospective and prospective studies. The mean difference (MD) and risk ratio (RR) with 95% confidence intervals (CI) were applied to assess the effectiveness of umifenovir for COVID-19. A total of 12 studies with 1052 patients were included in our final studies. Compared with control group, umifenovir was associated with higher negative rate of PCR on day 14 (RR:1.27; 95% CI: 1.04 to 1.55). However, umifenovir is not related to nucleus acid negative conversion time (MD: 0.09; 95% CI: -1.48 to 1.65), negative rate on day 7 (RR:1.09; 95% CI: 0.91 to 1.31), incidence of composite endpoint (RR:1.20; 95% CI: 0.61 to 2.37), rate of fever alleviation on day 7 (RR:1.00; 95% CI: 0.91 to 1.10), rate of cough alleviation on day 7 (RR:1.00; 95% CI: 0.85 to 1.18), or hospital length of stay (MD: 1.34; 95% CI: -2.08 to 4.76). Additionally, umifenovir was safe in COVID-19 patients (RR for incidence of adverse events: 1.29; 95% CI: 0.57 to 2.92). The results of sensitivity analysis and subgroup analysis were similar to pooled results. There is no evidence to support the use of umifenovir for improving patient-important outcomes in patients with COVID-19.

Inhaled corticosteroids and risk of lung cancer: A systematic review and meta-analysis.

INTRODUCTION: Current studies investigating the association between inhaled corticosteroid (ICS) use and risk of lung cancer have yielded inconsistent findings. The aim of this systematic review and meta-analysis was to pool all currently available data to estimate this association. METHODS: We systematically searched MEDLINE (1946 to July 2020), EMBASE (1974 to July 2020) and the Cochrane Library (June 2020) via Ovid to identify relevant articles investigating the association between the ICS use and the risk of lung cancer. Random-effects analysis was used to calculate pooled relative risks (RRs) with 95% confidence intervals (CIs). RESULTS: Ten articles including 234 920 patients were analysed. ICS use was identified to have a decreased risk of lung cancer in chronic obstructive pulmonary disease (8 studies, 1806 patients; RR = 0.73, 95% CI: 0.61-0.87, P < .01; I2  = 60.0 %), asthma (1 study, 41 438 patients; RR = 0.44, 95% CI: 0.34-0.57, P < .01) and mixed (1 study, 46 225 patients; RR = 0.79, 95% CI: 0.69-0.90, P < .01) patients. The findings of reduced risk of lung cancer were consistent in all subgroup analyses except for the short-term follow-up (≤5 years) (RR = 0.94, 95% CI: 0.81-1.07, P = .34) and free of immortal time bias (RR = 0.94, 95% CI: 0.82-1.08, P = .38) subgroups. CONCLUSIONS: The present study suggested that ICS use was associated with decreased risk of lung cancer. However, our findings should be interpreted with caution because most original studies were judged to be at high risk of immortal time bias.

Decreased mortality in acute respiratory distress syndrome patients treated with corticosteroids: an updated meta-analysis of randomized clinical trials with trial sequential analysis.

BACKGROUND: The possible benefits associated with corticosteroid treatment in acute respiratory distress syndrome (ARDS) patients are not fully known. We conducted an updated meta-analysis to assess the effect of corticosteroids in the treatment of patients with ARDS. METHODS: We systematically searched MEDLINE, Embase, and the Cochrane Library from inception to January 2021 via Ovid to identify randomized controlled trials evaluating the efficacy of glucocorticoids in the treatment of patients with ARDS. The primary outcome was hospital mortality. Secondary outcomes included the number of ventilator-free days at day 28, oxygenation improvement (PaO2/FIO2 ratios), and adverse events. RESULTS: Nine studies with 1371 participants were analyzed. The pooled analysis revealed that glucocorticoid use was associated with reduced mortality [relative risk (RR), 0.83; 95% confidence interval (CI) 0.74-0.93; P < 0.01; I2 = 37], and the statistical power was confirmed by trial sequential analysis. Glucocorticoids might also significantly increase the number of ventilator-free days at day 28 (mean deviation 3.66 days, 95% CI 2.64-4.68; P < 0.01) and improve oxygenation (standardized mean difference 4.17; 95% CI 2.32-6.02; P < 0.01). In addition, glucocorticoid use was not associated with increased risks of new infection (RR 0.84; 95% CI 0.70-1.01; P = 0.07) and hyperglycemia (RR 1.11; 95% CI 0.99-1.23; P = 0.06). CONCLUSIONS: The use of glucocorticoids might result in reduced mortality in patients with ARDS. Glucocorticoids might be recommended as an adjunct to standard care for ARDS; however, the optimal dose and duration of steroid therapy remains unknown and further studies are needed.

Clinical characteristics and risk factors associated with mortality in patients with severe community-acquired pneumonia and type 2 diabetes mellitus.

BACKGROUND: The present study was performed to investigate the impacts of type 2 diabetes mellitus (T2DM) on severe community-acquired pneumonia (SCAP) and to develop a novel prediction model for mortality in SCAP patients with T2DM. METHODS: This was a retrospective observational study conducted in consecutive adult patients with SCAP admitted to the intensive care unit (ICU) of West China Hospital, Sichuan University, China, between September 2011 and September 2019. The primary outcome was hospital mortality. A propensity score matching (PSM) analysis model with a 1:2 ratio was used for the comparisons of clinical characteristics and outcomes between T2DM and nondiabetic patients. The independent risk factors were identified via univariate and then multivariable logistic regression analysis and were then used to establish a nomogram. RESULTS: In total, 1262 SCAP patients with T2DM and 2524 matched patients without T2DM were included after PSM. Patients with T2DM had longer ICU length of stay (LOS) (13 vs. 12 days, P = 0.016) and higher 14-day mortality (15% vs. 10.8%, P < 0.001), 30-day mortality (25.7% vs. 22.7%, P = 0.046), ICU mortality (30.8% vs. 26.5%, P = 0.005), and hospital mortality (35.2% vs. 31.0%, P = 0.009) than those without T2DM. In SCAP patients with T2DM, the independent risk factors for hospital mortality were increased numbers of comorbidities and diabetes-related complications; elevated C-reactive protein (CRP), neutrophil to lymphocyte ratio (NLR), brain natriuretic peptide (BNP) and blood lactate; as well as decreased blood pressure on admission. The nomogram had a C index of 0.907 (95% CI: 0.888, 0.927) in the training set and 0.873 (95% CI: 0.836, 0.911) in the testing set, which was superior to the pneumonia severity index (PSI, AUC: 0.809, 95% CI: 0.785, 0.833). The calibration curve and decision curve analysis (DCA) also demonstrated its accuracy and applicability. CONCLUSIONS: SCAP patients with T2DM had worse clinical outcomes than nondiabetic patients. The nomogram has good predictive performance for hospital mortality and might be generally applied after more external validations.

Bronchus-blocked ultrasound-guided percutaneous transthoracic needle biopsy (BUS-PTNB) for intubated patients with severe lung diseases.

BACKGROUND: Examinations based on lung tissue specimen can play a significant role in the diagnosis for critically ill and intubated patients with lung infiltration. However, severe complications including tension pneumothorax and intrabronchial hemorrhage limit the application of needle biopsy. METHODS: A refined needle biopsy technique, named bronchus-blocked ultrasound-guided percutaneous transthoracic needle biopsy (BUS-PTNB), was performed on four intubated patients between August 2020 and April 2021. BUS-PTNB was done at bedside, following an EPUBNOW (evaluation, preparation, ultrasound location, bronchus blocking, needle biopsy, observation, and withdrawal of blocker) workflow. Parameters including procedure feasibility, sample acquisition, perioperative conditions, and complications were observed. Tissue specimens were sent to pathological examinations and microbial tests. RESULTS: Adequate specimens were successfully obtained from four patients. Diagnosis and treatment were correspondingly refined based on pathological and microbial tests. Intrabronchial hemorrhage occurred in patient 1 but was stopped by endobronchial blocker. Mild pneumothorax happened in patient 4 due to little air leakage, and closed thoracic drainage was placed. During the procedure, peripheral capillary hemoglobin oxygen saturation (SPO2), blood pressure, and heart rate of patient 4 fluctuated but recovered quickly. Vital signs were stable for patient 1-3. CONCLUSIONS: BUS-PTNB provides a promising, practical and feasible method in acquiring tissue specimen for critically ill patients under intratracheal intubation. It may facilitate the pathological diagnosis or other tissue-based tests for intubated patients and improve clinical outcomes.

Crazy-paving patterns as rare radiological manifestations of pulmonary cryptococcosis: a case report.

BACKGROUND: Crazy-paving patterns are rarely reported as radiological manifestations of pulmonary cryptococcosis. CASE PRESENTATION: Herein, we presented a very rare case of a crazy-paving pattern as a radiological manifestation of pulmonary cryptococcosis in a patient with primary ciliary dyskinesia. The diagnosis of pulmonary cryptococcosis and primary ciliary dyskinesia was ultimately confirmed by bronchoscopic biopsy, fungus culture, whole exome sequencing of blood, etc. The patient received flucytosine (PO, 5 g per day) and amphotericin B (IV, 70 mg per day) during hospitalization and sequential therapy with voriconazole (PO, 200 mg twice a day) after discharge. He recovered during follow-up. CONCLUSIONS: We concluded that pulmonary cryptococcosis should be considered a possible cause of crazy-paving patterns in chest CT scans.

A novel risk score to predict diagnosis with coronavirus disease 2019 (COVID-19) in suspected patients: A retrospective, multicenter, and observational study.

The aim of the study was to explore a novel risk score to predict diagnosis with COVID-19 among all suspected patients at admission. This was a retrospective, multicenter, and observational study. The clinical data of all suspected patients were analyzed. Independent risk factors were identified via multivariate logistic regression analysis. Finally, 336 confirmed COVID-19 patients and 139 control patients were included. We found nine independent risk factors for diagnosis with COVID-19 at admission to hospital: epidemiological exposure histories (OR:13.32; 95%CI, 6.39-27.75), weakness/fatigue (OR:4.51, 95%CI, 1.70-11.96), heart rate less than 100 beat/minutes (OR:3.80, 95%CI, 2.00-7.22), bilateral pneumonia (OR:3.60, 95%CI, 1.83-7.10), neutrophil count less than equal to 6.3 × 109 /L (OR: 6.77, 95%CI, 2.52-18.19), eosinophil count less than equal to 0.02 × 109 /L (OR:3.14, 95%CI, 1.58-6.22), glucose more than equal to 6 mmol/L (OR:2.43, 95%CI, 1.04-5.66), D-dimer ≥ 0.5 mg/L (OR:3.49, 95%CI, 1.22-9.96), and C-reactive protein less than 5 mg/L (OR:3.83, 95%CI, 1.86-7.92). As for the performance of this risk score, a cut-off value of 20 (specificity: 0.866; sensitivity: 0.813) was identified to predict COVID-19 according to reciever operator characteristic curve and the area under the curve was 0.921 (95%CI: 0.896-0.945; P < .01). We designed a novel risk score which might have a promising predictive capacity for diagnosis with COVID-19 among suspected patients.

Correction to: The effect of corticosteroids on the mortality of patients with influenza pneumonia: a systematic review and meta-analysis.

An amendment to this paper has been published and can be accessed via the original article.

Practical strategies to reduce nosocomial transmission to healthcare professionals providing respiratory care to patients with COVID-19.

Coronavirus disease (COVID-19) is an emerging viral infection that is rapidly spreading across the globe. SARS-CoV-2 belongs to the same coronavirus class that caused respiratory illnesses such as severe acute respiratory syndrome (SARS) and Middle East respiratory syndrome (MERS). During the SARS and MERS outbreaks, many frontline healthcare workers were infected when performing high-risk aerosol-generating medical procedures as well as when providing basic patient care. Similarly, COVID-19 disease has been reported to infect healthcare workers at a rate of ~ 3% of cases treated in the USA. In this review, we conducted an extensive literature search to develop practical strategies that can be implemented when providing respiratory treatments to COVID-19 patients, with the aim to help prevent nosocomial transmission to the frontline workers.