Risk Of In-hospital Mortality Research Articles

Prioritizing patients for hip fracture surgery: the role of frailty and cardiac risk.

The number of patients with hip fractures continues to rise as the average age of the population increases. Optimizing outcomes in this cohort is predicated on timely operative repair. The aim of this study was to determine if patients with hip fractures who are frail or have a higher cardiac risk suffer from an increased risk of in-hospital mortality when surgery is postponed >24 h. All patients registered in the 2013-2021 TQIP dataset who were ≥65 years old and underwent surgical fixation of an isolated hip fracture caused by a ground-level fall were included. Adjustment for confounding was performed using inverse probability weighting (IPW) while stratifying for frailty with the Orthopedic Frailty Score (OFS) and cardiac risk using the Revised Cardiac Risk Index (RCRI). The outcome was presented as the absolute risk difference in in-hospital mortality. A total of 254,400 patients were included. After IPW, all confounders were balanced. A delay in surgery was associated with an increased risk of in-hospital mortality across all strata, and, as the degree of frailty and cardiac risk increased, so too did the risk of mortality. In patients with OFS ≥4, delaying surgery >24 h was associated with a 2.33 percentage point increase in the absolute mortality rate (95% CI: 0.57-4.09, p = 0.010), resulting in a number needed to harm (NNH) of 43. Furthermore, the absolute risk of mortality increased by 4.65 percentage points in patients with RCRI ≥4 who had their surgery delayed >24 h (95% CI: 0.90-8.40, p = 0.015), resulting in a NNH of 22. For patients with OFS 0 and RCRI 0, the corresponding NNHs when delaying surgery >24 h were 345 and 333, respectively. Delaying surgery beyond 24 h from admission increases the risk of mortality for all geriatric hip fracture patients. The magnitude of the negative impact increases with the patient's level of cardiac risk and frailty. Operative intervention should not be delayed based on frailty or cardiac risk.

Difference in mortality risk predicted by leukocyte and lymphocyte levels in COVID-19 patients infected with the Wild-type, Delta, and Omicron strains.

This study aimed to investigate the changing trends, level differences, and prognostic performance of the leukocyte and lymphocyte levels of patients infected with the Wild strains, Delta strains and Omicron strains to provide a reference for prognostic assessment. In the current study, we conducted a retrospective cross-sectional study to evaluate the changing trends, level differences, and prognostic performance of leukocyte and lymphocyte of different strains at admission and discharge may already exist in patients with coronavirus disease-2019 (COVID-19) infected with the Wild type, Delta, and Omicron strains. A retrospective cross-sectional study was conducted. We recruited and screened the 243 cases infected with the Wild-type strains in Wuhan, the 629 cases infected with the Delta and 116 cases infected strains with the Omicron strains in Xi'an. The leukocyte and lymphocyte levels were compared the cohort of Wild-type infection with the cohort of Delta and the Omicron. The changes in the levels of leukocytes and lymphocytes exhibit a completely opposite trend in patients with COVID-19 infected with the different strains. The lymphocyte level at admission and discharge in patients with COVID-19 infected with Omicron strains (area under curve [AUC] receiver operating characteristic curve [ROC] 72.8-90.2%, 82.8-97.2%) presented better performance compared patients with COVID-19 infected with Wild type strains (AUC ROC 60.9-80.7%, 82.3-97.2%) and Delta strains (AUC ROC 56.1-84.7%, 40.3-93.3%). Kaplan-Meier curves showed that the leukocyte levels above newly established cutoff values and the lymphocyte levels below newly established cutoff values had a significantly higher risk of in-hospital mortality in COVID-19 patients with Wild-type and Omicron strains (P < .01). The levels of leukocyte and lymphocyte at admission and discharge in patients with COVID-19 infected with the Wild type, Delta, and Omicron strains may be differences among strains, which indicates different death risks. Our research may help clinicians identify patients with a poor prognosis for severe acute respiratory syndrome coronavirus 2 infection.

Association between albumin-corrected anion gap and in-hospital mortality of intensive care patients with trauma: A retrospective study based on MIMIC-Ⅲ and Ⅳ databases.

To investigate the correlation between albumin-corrected anion gap(ACAG) within the first 24 hours of admission and in-hospital mortality in trauma patients in intensive care unit(ICU). We utilized the MIMIC-Ⅲ and MIMIC-Ⅳ databases to examine trauma patients admitted to the ICU. The relationship between ACAG and in-hospital mortality in trauma patients was analyzed using Receiver Operating Characteristic(ROC) curve, Kaplan-Meier (K-M) survival curve, and Cox regression model. Propensity score matching (PSM) and subgroup analysis were conducted to enhance stability and reliability of the findings. Mortality at 30-day and 90-day served as secondary outcomes. The study enrolled a total of 1038 patients. The AUC for ACAG (0.701, 95%CI: 0.652-0.749) was notably higher than that for anion gap and albumin. The Log-rank test revealed that the optimal cut-off point of ACAG for predicting in-hospital mortality was determined to be 20.375mmol/L. The multivariate Cox regression analysis demonstrated an independent association between high ACAG level and a higher risk of in-hospital mortality (HR = 3.128, 95% CI: 1.615-6.059). After PSM analysis, a matched cohort consisting of 291 subjects was obtained. We found no signifcant interaction in most stratas. Finally, The in-hospital, 30-day, and 90-day survival rates in the high ACAG group exhibited a statistically decrease compared to those in the low ACAG group both pre- and post-matching. The elevated level of ACAG was found to be independently associated with increased in-hospital mortality among trauma patients in the ICU.

Predicting sepsis in-hospital mortality with machine learning: a multi-center study using clinical and inflammatory biomarkers

BackgroundThis study aimed to develop and validate an interpretable machine-learning model that utilizes clinical features and inflammatory biomarkers to predict the risk of in-hospital mortality in critically ill patients suffering from sepsis.MethodsWe enrolled all patients diagnosed with sepsis in the Medical Information Mart for Intensive Care IV (MIMIC-IV, v.2.0), eICU Collaborative Research Care (eICU-CRD 2.0), and the Amsterdam University Medical Centers databases (AmsterdamUMCdb 1.0.2). LASSO regression was employed for feature selection. Seven machine-learning methods were applied to develop prognostic models. The optimal model was chosen based on its accuracy, F1 score and area under curve (AUC) in the validation cohort. Moreover, we utilized the SHapley Additive exPlanations (SHAP) method to elucidate the effects of the features attributed to the model and analyze how individual features affect the model’s output. Finally, Spearman correlation analysis examined the associations among continuous predictor variables. Restricted cubic splines (RCS) explored potential non-linear relationships between continuous risk factors and in-hospital mortality.Results3535 patients with sepsis were eligible for participation in this study. The median age of the participants was 66 years (IQR, 55–77 years), and 56% were male. After selection, 12 of the 45 clinical parameters collected on the first day after ICU admission remained associated with prognosis and were used to develop machine-learning models. Among seven constructed models, the eXtreme Gradient Boosting (XGBoost) model achieved the best performance, with an AUC of 0.94 and an F1 score of 0.937 in the validation cohort. Feature importance analysis revealed that Age, AST, invasive ventilation treatment, and serum urea nitrogen (BUN) were the top four features of the XGBoost model with the most significant impact. Inflammatory biomarkers may have prognostic value. Furthermore, SHAP force analysis illustrated how the constructed model visualized the prediction of the model.ConclusionsThis study demonstrated the potential of machine-learning approaches for early prediction of outcomes in patients with sepsis. The SHAP method could improve the interoperability of machine-learning models and help clinicians better understand the reasoning behind the outcome.

Real-time prognostic biomarkers for predicting in-hospital mortality and cardiac complications in COVID-19 patients.

Hospitalized patients with Coronavirus disease 2019 (COVID-19) are highly susceptible to in-hospital mortality and cardiac complications such as atrial arrhythmias (AA). However, the utilization of biomarkers such as potassium, B-type natriuretic peptide, albumin, and others for diagnosis or the prediction of in-hospital mortality and cardiac complications has not been well established. The study aims to investigate whether biomarkers can be utilized to predict mortality and cardiac complications among hospitalized COVID-19 patients. Data were collected from 6,927 hospitalized COVID-19 patients from March 1, 2020, to March 31, 2021 at one quaternary (Henry Ford Health) and five community hospital registries (Trinity Health Systems). A multivariable logistic regression prediction model was derived using a random sample of 70% for derivation and 30% for validation. Serum values, demographic variables, and comorbidities were used as input predictors. The primary outcome was in-hospital mortality, and the secondary outcome was onset of AA. The associations between predictor variables and outcomes are presented as odds ratio (OR) with 95% confidence intervals (CIs). Discrimination was assessed using area under ROC curve (AUC). Calibration was assessed using Brier score. The model predicted in-hospital mortality with an AUC of 90% [95% CI: 88%, 92%]. In addition, potassium showed promise as an independent prognostic biomarker that predicted both in-hospital mortality, with an AUC of 71.51% [95% Cl: 69.51%, 73.50%], and AA with AUC of 63.6% [95% Cl: 58.86%, 68.34%]. Within the test cohort, an increase of 1 mEq/L potassium was associated with an in-hospital mortality risk of 1.40 [95% CI: 1.14, 1.73] and a risk of new onset of AA of 1.55 [95% CI: 1.25, 1.93]. This cross-sectional study suggests that biomarkers can be used as prognostic variables for in-hospital mortality and onset of AA among hospitalized COVID-19 patients.

Baseline and average platelet count can predict the outcome of patients with aneurysmal subarachnoid hemorrhage

BackgroundBaseline values and the change of platelet count (PLT) during disease were reported to be associated with prognosis of patients with cancer and intensive care treatment. We aimed to evaluate the association between PLT with the course and prognosis of aneurysmal subarachnoid hemorrhage (SAH). MethodsAdmission (AdmPLT) and the 14-days mean PLT (MeanPLT) values of 763 SAH patients treated between 01/2005 and 06/2016 were recorded and, for further analysis, divided into four categories: <150, 150–260, 261–400 and > 400 × 109/L. Primary endpoints were cerebral infarcts in follow-up computed tomography scans, in-hospital mortality and unfavorable outcome at 6-months follow-up defined as modified Rankin scale>3. Adverse events during SAH were assessed as secondary endpoints. ResultsHigher PLT values were independently associated with lower risk of cerebral infarction (MeanPLT: aOR = 0.65 per-PLT-category-increase, p = 0.001), in-hospital mortality (AdmPLT: aOR = 0.64, p = 0.017; MeanPLT: aOR = 0.23, p < 0.0001) and unfavorable outcome (AdmPLT: aOR = 0.70, p = 0.031; MeanPLT: aOR = 0.35, p < 0.0001). Moreover, individuals with poorer outcome were less prone to PLT increase during SAH (mean values: -+20.3 vs + 30.5 × 109/L for cerebral infarction; +9.3 vs + 32.8 × 109/L for in-hospital mortality; +14.4 vs + 31.1 × 109/L for unfavorable outcome). The following adverse events during SAH were related to AdmPLT and/or MeanPLT: non-aneurysm related secondary rebleeding, intracranial hypertension requiring conservative treatment or decompressive craniectomy, sepsis and acute kidney failure. ConclusionLow PLT at admission and their less prominent increase during SAH were strongly linked with poor outcome of SAH. Further analysis is required to clarify the background of this association and potential therapeutic implications.

Higher mortality and longer length of stay in hospitalized patients with newly diagnosed diabetes

AimsWe investigated the association between diabetes status at admission and in-hospital outcomes in all hospitalized patients, regardless of the reason for admission. MethodsAll individuals aged 20 years or older who were admitted to Yongin Severance Hospital between March 2020 and February 2022 were included in study. Subjects were categorized into three groups: non-DM, known DM, and newly diagnosed DM, based on medical history, anti-diabetic medications use, and laboratory test. Hospitalization-related outcomes, including in-hospital mortality and length of hospital stay, were compared between groups. Results33,166 participants were enrolled. At hospitalization, 6,572 (19.8 %) subjects were classified as known DM, and another 2,634 (7.9 %) subjects were classified as newly diagnosed DM. In-hospital mortality was highest in newly diagnosed DM (HR 1.89, 95% CI 1.58–2.26, p < 0.001) followed by known DM (HR 1.41, 95% CI 1.18–1.69, p < 0.001) compared to non-DM. Length of hospital stay was significantly longer in newly diagnosed DM (median [IQR] 9.0 [5.0–18.0],days) than known DM (median [IQR] 5.0 [3.0–10.0],days)(p < 0.001) and non-DM (median [IQR] 4.0 [2.0–7.0],days). After adjusting for multiple covariates, newly diagnosed diabetes was independently associated with increased in-hospital mortality (p < 0.001). ConclusionsDiabetes status at admission was closely linked to hospitalization-related outcomes. Notably, individuals with newly diagnosed diabetes demonstrated a higher risk of in-hospital mortality and a prolonged length of hospital stay.

Association of the triglyceride-glucose index with severity of coronary stenosis and in-hospital mortality in patients with acute ST elevation myocardial infarction after percutaneous coronary intervention: a multicentre retrospective analysis cohort study

ObjectivesTo explore the impact of the triglyceride-glucose (TyG) index on the severity of coronary stenosis and the risk of in-hospital mortality in patients with acute ST segment elevation myocardial infarction...

The evaluation of frequency and predictors of delirium and its short-term and long-term outcomes in hospitalized older adults’

BackgroundDelirium is a common complication in hospitalized older adults with multifactorial etiology and poor health outcomes. AimTo determine the frequency and predictors of delirium and its short-term and long-term outcomes in hospitalized older adults. MethodsA prospective observational study was performed in patients aged ≥60 years consecutively admitted to geriatric ward. Potential risk factors were assessed within 24 hours of hospital admission. Delirium screening was performed on admission and daily thereafter throughout the hospital stay using Confusion Assessment Method (CAM). Patients were followed up at 1-year post-discharge. ResultsThe study included 200 patients with mean age 73.1 ± 8.83 years. Incidence and prevalence rate of delirium were 5% and 20% respectively. Multivariable regression analysis revealed emergency admission (OR= 5.12 (1.94–13.57), p=0.001), functional dependency (Katz index of Independence in Activities of Daily Living (Katz-ADL) score <5) 2 weeks before admission (OR= 3.08 (1.30–7.33), p=0.011) and more psychopathological symptoms (higher Brief Psychiatric Rating Scale (BPRS) total score) (OR=1.12 (1.06–1.18), p=0.001) to be independently associated with delirium. Patients in delirium group had significantly high in-hospital mortality (OR= 5.02 (2.12–11.8), p=0.001) and post-discharge mortality (HR= 2.02 (1.13–3.61), p=0.017) and functional dependency (Katz-ADL score <5) (OR= 5.45 (1.49–19.31), p=0.01) at 1-year follow up. ConclusionDelirium is quite frequent in geriatric inpatients and is associated with high in-hospital and post-discharge mortality risk and long-term functional dependency. Emergency admission, pre-hospitalization functional dependency, and more general psychopathological symptoms are independently associated factors. Hence, earliest identification and treatment with early implementation of rehabilitation services is warranted.

Investigating the Impact of Cannabis Consumption on Hospital Outcomes in Patients With Primary Spontaneous Pneumothorax: A Nationwide Analysis.

Introduction Existing data suggest an association between primary spontaneous pneumothorax (PSP) and cannabis consumption, although evidence remains controversial. Methods This study used the 2016-2019 National Inpatient Sample Database to examine inpatients with PSP, categorizing them as cannabis users and non-users. Multivariate regression analyzed continuous variables, chi-square assessed categorical variables, and logistic regression models were built. Propensity score matching (PSM) mitigated the confounding bias. Results A total of 399,495 patients with PSP were admitted during the study period (13,415 cannabis users and 386,080 non-cannabis users). Cannabis users were more likely to be younger (p<0.001) and male (p<0.001) with a lower risk of baseline comorbidities than non-users. Cannabis users had a lower risk of sudden cardiac arrest, vasopressor use, the development of acute kidney injury, venous thromboembolism, the requirement for invasive and non-invasive mechanical ventilation, hemodialysis, ventilator-associated pneumonia, and the need for a tracheostomy. Cannabis use was associated with a 3.4 days shorter hospital stay (p<0.001), as confirmed by PSM analysis (2.3 days shorter, p<0.001). Additionally, cannabis users showed a lower risk of in-hospital mortality (p<0.001), a trend maintained in the PSM analysis (p<0.001). Conclusions Our study revealed correlations suggesting that cannabis users with PSP might experience lower in-hospital mortality and fewer complications than non-cannabis users.

Impact of Frailty on Inpatient Outcomes of Acute Traumatic Spinal Cord Injury: Evidence From US National Inpatient Sample.

Spinal cord injury (SCI) is any spinal cord injury or affliction that results in temporary or permanent impairment of motor or sensory function. This study determined the prevalence of frailty and its impact on in-hospital outcomes of patients admitted with acute traumatic SCI (TSCI). This retrospective study extracted data of adults 18 to 85 years with acute TSCI from the US Nationwide Inpatient Sample (NIS) 2016 to 2018. Frailty status were assessed by the 11-factor modified Frailty Index (mFI-11) through claim codes. Patients with an mFI ≥3 were classified as frail. Associations between study variables and in-hospital mortality, discharge status, prolonged length of stay, severe infection, and hospital costs were determined by univariate and multivariable regression analyses. A total of 52,263 TSCI patients were identified, where 12,203 (23.3%) patients were frail. After adjusting for relevant confounders, frailty was independently associated with increased risk for in-hospital mortality [adjusted odds ratio (aOR) = 1.25, 95% CI:1.04-1.49], unfavorable discharge (aOR =1.15, 95% CI: 1.09-1.22), prolonged length of stay (aOR =1.32, 95% CI: 1.24-1.40), and severe infection (aOR =2.52, 95% CI: 2.24-2.83), but not hospital cost. Stratified analyses revealed frailty was associated with higher unfavorable discharge and severe infection regardless of age, Charlson Comorbidity Index, and injury level. In acute TSCI, frailty is independently associated with increased risk for adverse inpatient outcomes in terms of in-hospital mortality, prolonged hospital stays, unfavorable discharge, and particularly severe infection.

Enhancing predictions with a stacking ensemble model for ICU mortality risk in patients with sepsis-associated encephalopathy.

We identified predictive factors and developed a novel machine learning (ML) model for predicting mortality risk in patients with sepsis-associated encephalopathy (SAE). In this retrospective cohort study, data from the Medical Information Mart for Intensive Care IV (MIMIC-IV) and eICU Collaborative Research Database were used for model development and external validation. The primary outcome was the in-hospital mortality rate among patients with SAE; the observed in-hospital mortality rate was 14.74% (MIMIC IV: 1112, eICU: 594). Using the least absolute shrinkage and selection operator (LASSO), we built nine ML models and a stacking ensemble model and determined the optimal model based on the area under the receiver operating characteristic curve (AUC). We used the Shapley additive explanations (SHAP) algorithm to determine the optimal model. The study included 9943 patients. LASSO identified 15 variables. The stacking ensemble model achieved the highest AUC on the test set (0.807) and 0.671 on external validation. SHAP analysis highlighted Glasgow Coma Scale (GCS) and age as key variables. The model (https://sic1.shinyapps.io/SSAAEE/) can predict in-hospital mortality risk for patients with SAE. We developed a stacked ensemble model with enhanced generalization capabilities using novel data to predict mortality risk in patients with SAE.

Modification of qSOFA Increases Mortality Prediction Accuracy in Sepsis Patients

The Quick Sequential Organ Failure Assessment (qSOFA) is a simple parameter, however, its sensitivity as a mortality predictor is low. This study aimed to improve the predictor performance of qSOFA for in-hospital mortality. This study was a retrospective single-centered cohort using medical record data. This study included 150 patients aged 18-80 years old, who experienced sepsis and received ICU care between September 2021 and August 2022. qSOFA and modified ROX index (mROX) were calculated based on the most severe condition recorded in the emergency department (ED). Each variable's area under the receiver operating characteristic (AUROC) curve, sensitivity, and specificity were compared to predict in-hospital mortality. qSOFA scores ≥2 and mROX values ≤3.20 were independent factors that increased the risk of in-hospital mortality (OR 3.69 and 21.50; p 0.004 and 0.005, respectively). The combination of qSOFA scores ≥2 and mROX value ≤3.20 as in-hospital mortality predictors resulted in AUROC 0.791 with a sensitivity of 71.7% and specificity of 75.7%. The AUROC, sensitivity, and specificity of the combination of qSOFA and mROX were higher than qSOFA (0.766, 70.8%, 70.3%) or mROX (0.760, 68.1%, 67.6%) alone. In conclusion, the combination of qSOFA scores ≥2 and mROX values ≤3.20 increase the sensitivity and specificity for predicting in-hospital mortality in sepsis patients.

Association between serum anion gap and risk of in-hospital mortality in patients with acute heart failure

A high serum anion gap (AG) at the time of patient admission can lead to the deterioration or even death; data are lacking for patients who suffer acute heart failure (AHF). The present study aimed at exploring the impact of serum AG (SAG) levels on the in-hospital mortality in AHF patients. The study conducted retrospective analysis on the data from the medical information mart for intensive care (MIMIC-IV) database in severe AHF cases. Serum AG, age, sex, concomitant diseases and laboratory tests were collected from patients at admission. Multivariate Cox proportional hazard regression model together with Kaplan Meier (K–M) survival curve served for analyzing the relationship of serum AG with the hospital all-cause mortality (ACM). In addition, subgroup analysis assisted in assessing the concordance. Data from 2774 AHF patients were collected in the study. The hospital ACM rate was 9.2% (254/2774). After correcting potential confounders, multivariate analysis compared the high serum AG level (≥ 16 mmol/L) and the low serum AG level (< 16 mmol/L) (hazard ratio (HR): 1.89 [95% CI 1.42–2.51]). In a similar way, K–M survival curve indicated that hospital survival was lower in patients with high serum, suggesting that high serum AG level could lead to poor AHF prognosis. In patients with AHF, high serum AG level could increase the hospital ACM.

Early pandemic in-hospital outcomes and mortality risk factors in COVID-19 solid organ transplant patients

Background Solid organ transplant (SOT) recipients with COVID-19 have a higher risk of mortality than those without COVID-19. However, it is unclear how SOT patient outcomes compare to the general population without SOT who contract COVID-19. Methods We used the National Inpatient Sample from January to December 2020 to investigate inpatient outcomes seen in SOT recipients after contracting COVID-19 compared to nontransplant patients. We identified our study sample using ICD-10 CM and excluded those <18 years of age and those with dual organ transplants. Inpatient outcomes were compared in SOT and non-SOT COVID cohorts, and we further evaluated predictors of mortality in the SOT with COVID population. Results Out of the 1,416,445 COVID-19 admissions included in the study, 8315 (0.59%) were single SOT recipients. Our analysis that adjusted for multiple baseline characteristics and comorbidities demonstrated that COVID-19 in SOT patients was associated with higher rates of acute kidney injury (adjusted odds ratio [aOR] 2.34, 95% confidence interval [CI] 1.81–3.02, P < 0.01), lower rates of acute respiratory distress syndrome (aOR 0.68, 95% CI 0.54–0.85, P < 0.01), and similar rates of cardiac arrest, pulmonary embolism, circulatory shock, cerebrovascular events, and in-hospital mortality. Age >65 was associated with mortality in SOT patients. Conclusion In this nationally representative sample, SOT patients presenting with COVID-19 experienced similar rates of mortality compared to those without SOT. SOT patients were more likely to develop acute kidney injury. Further research is needed to understand the complex relationship between transplant patient outcomes and COVID-19.

Correlation of the serum cell division cycle 42 with CD4+ T cell subsets and in-hospital mortality in Stanford type B aortic dissection patients.

Cell division cycle 42 (CDC42) regulates CD4+ T-cell differentiation and participates in vascular stiffness and atherosclerosis and is involved in the progression of Stanford type B aortic dissection (TBAD). This study aimed to explore the correlation between serum CDC42 level and CD4+ T cell subsets and in-hospital mortality in TBAD patients. Serum CDC42 and peripheral blood T-helper (Th) 1, Th2, and Th17 cells were detected in 127 TBAD patients by enzyme-linked immunosorbent assay and flow cytometry, respectively. Serum CDC42 was also quantified in 30 healthy controls. Serum CDC42 was decreased in TBAD patients vs. healthy controls (median [interquartile range (IQR)]: 418.0 (228.0-761.0) pg/ml vs. 992.0 (716.3-1,445.8) pg/ml, P < 0.001). In TBAD patients, serum CDC42 was negatively correlated with Th17 cells (P = 0.001), but not Th1 (P = 0.130) or Th2 cells (P = 0.098). Seven (5.5%) patients experienced in-hospital mortality. Serum CDC42 was reduced in patients who experienced in-hospital mortality vs. those who did not (median (IQR): 191.0 (145.0-345.0) pg/ml vs. 451.5 (298.3-766.8) pg/ml, P = 0.006). By receiver operating characteristic analysis, serum CDC42 showed a good ability for estimating in-hospital mortality [area under curve = 0.809, 95% confidence interval (CI) = 0.662-0.956]. By the multivariate logistic regression analysis, elevated serum CDC42 [odd ratio (OR) = 0.994, 95% CI = 0.998-1.000, P = 0.043] was independently correlated with lower risk of in-hospital mortality, while higher age (OR = 1.157, 95% CI = 1.017-1.316, P = 0.027) was an independent factor for increased risk of in-hospital mortality. Serum CDC42 negatively associates with Th17 cells and is independently correlated with decreased in-hospital mortality risk in TBAD patients.

Prognostic Factors for Mortality in Adults Hospitalized With COVID-19 Infection in the Americas.

Many reports on COVID-19have highlighted an association between patient characteristics upon admission for COVID-19 and how they affect variable progressions of the disease severity and, ultimately mortality. In this cohort, we analyzed data from patients in the Americas who tested positive for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the virus that causes COVID-19, and were hospitalized. We retrieved data from identified studies selected from full-text screening to identify relevant patient demographics: age, gender, race, duration of hospital stay, and comorbidities. Data were assessed for consistency, missing values, presence of outliers, and implausible values were documented. Our analysis was performed by calculating the case fatality rates for data reported by the original authors within the studies. The primary outcome was mortality. Participants in the review were COVID-19 inpatients diagnosed through reverse-transcriptase polymerase chain reaction tests (RT-PCR) and above the age of 45 years in the North and South American continents. The study included 12,895 patients who were hospitalized with the COVID-19 infection, and an overall 14.4% in-hospital case-fatality rate was demonstrated. The prognostic factor with the most significant association for increased risk of in-hospital mortality in the elderly was having a high viral load of the COVID-19 virus with a case-fatality rate of 41.4%. In addition, being of Caucasian ethnicity and having the comorbidity of chronic obstructive pulmonary disease (COPD) were also associated with higher rates of mortality among elderly hospitalized patients with case-fatality rates of 40.8% and 35.7% respectively. Prognostic factors for COVID-19 mortality were determined by having a high viral load of the infection, being of Caucasian ethnicity, and having underlying pulmonary comorbidities such as COPD. In the future research should be undertaken to determine the underlying mechanisms behind these effects.

Current situation of management of spontaneous pneumothorax in Japan: A cross-sectional cohort study

BackgroundLimited epidemiological information is available on spontaneous pneumothorax. To address this gap, the Japan Society for Pneumothorax and Cystic Lung Disease (JSPCLD) conducted a nationwide retrospective survey to investigate the current epidemiology of spontaneous pneumothorax in Japan. MethodsIn this study, we conducted a retrospective cross-sectional cohort study to demonstrate the clinical features of spontaneous pneumothorax in one year from April 2019 to March 2020, compare patient characteristics and treatment outcomes between primary (PSP) and secondary spontaneous pneumothorax (SSP), and investigate the risk factors associated with in-hospital mortality among patients with SSP. ResultsA total of 1784 patients from 28 institutions were enrolled in the study, with PSP observed in 956 cases (53.6%) and SSP in 817 cases (45.8%). The age distribution showed a biphasic peak caused by the different peaks between PSP and SSP. In-hospital mortality occurred in 42 cases (2.4%) among all patients, with 0 cases (0%) in PSP and 42 cases (5.1%) in SSP. Multivariable analyses revealed that interstitial pneumonia as an underlying disease (odds ratio: 2.4700, 95% confidence interval: 1.1100 to 5.4800, p = 0.0269), performance status≧3 (odds ratio: 7.3900, 95% confidence interval: 3.1900 to 17.2000, p < 0.0001), and lower value of serum albumin on admission (odds ratio: 0.4060, 95% confidence interval: 0.2140 to 0.7690, p = 0.0057) were significantly associated with in-hospital mortality among patients with SSP. ConclusionsSSP patients with poor baseline conditions are at a higher risk for in-hospital mortality. It is crucial to provide close and meticulous management for SSP patients with compromised conditions.

Classification of the Urgency of the Procedure and Outcome of Acute Type A Aortic Dissection

Surgery for type A aortic dissection (TAAD) is associated with high risk of early mortality. The prognostic impact of a new classification of the urgency of the procedure was evaluated in this multicenter cohort study.Data on consecutive patients who underwent surgery for acute TAAD were retrospectively collected in the multicenter, retrospective European Registry of TAAD (ERTAAD).The rates of in-hospital mortality of 3902 consecutive patients increased along with the ERTAAD procedure urgency grades: urgent procedure, 10.0%; emergency procedure grade 1, 13.3%; emergency procedure grade 2, 22.1%; salvage procedure grade 1, 45.6%; and salvage procedure grade 2, 57.1% (p<0.0001). Preoperative arterial lactate correlated with the urgency grades. Inclusion of the ERTAAD procedure urgency classification significantly improved the area under the receiver operating characteristics curves of the regression model as well as the integrated discrimination indices and the net reclassification indices. The risk of postoperative stroke/global brain ischemia, mesenteric ischemia, lower limb ischemia, dialysis, and acute heart failure increased along with the urgency grades.In conclusion, the urgency of surgical repair of acute TAAD seems to have a significant impact on the risk of in-hospital mortality may be useful to improve the stratification of the operative risk of these critically ill patients. This study showed that salvage surgery for TAAD is justified because half of patients may survive to discharge.

4C mortality score and COVID-19 mortality risk score: an analysis in four different age groups of an Italian population.

To evaluate the prognostic stratification ability of 4C Mortality Score and COVID-19 Mortality Risk Score in different age groups. Retrospective study, including all patients, presented to the Emergency Department of the University Hospital Careggi, between February, 2020 and May, 2021, and admitted for SARS-CoV2. Patients were divided into four subgroups based on the quartiles of age distribution: patients < 57years (G1, n = 546), 57-71years (G2, n = 508), 72-81years (G3, n = 552), and > 82years (G4, n = 578). We calculated the 4C Mortality Score and COVID-19 Mortality Risk Score. The end-point was in-hospital mortality. In the whole population (age 68 ± 16years), the mortality rate was 19% (n = 424), and increased with increasing age (G1: 4%, G2: 11%, G3: 22%, and G4: 39%, p < 0.001). Both scores were higher among non-survivors than survivors in all subgroups (4C-MS, G1: 6 [3-7] vs 3 [2-5]; G2: 10 [7-11] vs 7 [5-8]; G3: 11 [10-14] vs 10 [8-11]; G4: 13 [12-15] vs 11 [10-13], all p < 0.001; COVID-19 MRS, G1: 8 [7-9] vs 9 [9-11], G2: 10 [8-11] vs 11 [10-12]; G3: 11 [10-12] vs 12 [11-13]; G4: 11 [10-13] vs 13 [12-14], all p < 0.01). The ability of both scores to identify patients at higher risk of in-hospital mortality, was similar in different age groups (4C-MS: G1 0.77, G2 0.76, G3 0.68, G4 0.72; COVID-19 MRS: G1 0.67, G2 0.69, G3 0.69, G4 0.72, all p for comparisons between subgroups = NS). Both scores confirmed their good performance in predicting in-hospital mortality in all age groups, despite their different mortality rate.