In-hospital Mortality In Patients Research Articles

Association between blood–urea–nitrogen-to-albumin ratio and in-hospital mortality in patients diagnosed with coronavirus disease 2019: a retrospective cohort study

BackgroundThe blood–urea–nitrogen-to-albumin ratio (BAR) is recognized as a novel prognostic indicator; however, there is a limited number of studies investigating the relationship between BAR and in-hospital mortality associated with coronavirus disease 2019 (COVID-19). Therefore, the present investigation aims to explore the correlation between BAR and in-hospital mortality in patients with COVID-19 in China.MethodsThis retrospective observational study enrolled a cohort of 1027 patients diagnosed with COVID-19 between December 2022 and March 2023. Multivariate Cox regression analyses were used to ascertain the independent association between BAR and in-hospital mortality among patients with COVID-19. Furthermore, stratified analyses were used to investigate potential interaction effects with variables, such as age, sex, COVID-19 Severity, hypertension, coronary artery disease, and diabetes mellitus.ResultsA total of 117 patients (11.4%) died from various causes during hospitalization. Subsequent to adjustment for confounding variables, patients in the highest BAR tertile exhibited an elevated risk for in-hospital mortality relative to those in the lowest tertile (hazard ratio [HR] 2.44 [95% confidence interval CI 1.24–4.79]) when BAR was treated as a categorical variable. When considering BAR as a continuous variable, a 6% increase in the prevalence of in-hospital mortality was observed for each 1-unit increase in BAR (adjusted HR 1.06 [95% CI 1.03–1.08]; P < 0.001). Stratified analyses revealed a consistent association between BAR and in-hospital mortality due to COVID-19.ConclusionsBAR exhibited a significant relationship with in-hospital mortality in patients with COVID-19, suggesting that a higher BAR is associated with a poorer prognosis. However, further research is required to confirm these findings.

Predictors of in hospital mortality in cardiogenic shock following ST-elevation myocardial infarction (STEMI)

Objectives: Cardiogenic shock (CS) is a severe complication of ST-Elevation Myocardial Infarction (STEMI) and is associated with high mortality rates. This study aimed to identify predictors of in-hospital mortality in patients experiencing cardiogenic shock following STEMI. Methods: A retrospective cohort study was conducted at Afshar Hospital in Yazd from 2018 to 2023, analyzing clinical data from 62 STEMI patients diagnosed with CS. The mean age of the cohort was 64.9 ± 13.5 years, with a male predominance of 69.5%. Key variables assessed included left ventricular ejection fraction (LVEF), comorbidities, and treatment interventions. Survival analysis and Cox regression were employed to evaluate mortality outcomes. Results: LVEF less than 30% emerged as a significant predictor of in-hospital mortality, with a hazard ratio of 11.1 (95% CI: 2.6–47.4; p &lt; 0.001). The mean survival time was 7.0 days (95% CI: 5.8–8.3). Additionally, hyperlipidemia was associated with increased mortality, presenting an odds ratio of 11.3 (95% CI: 1.1–114.1; p = 0.040). The prevalence of chronic kidney disease was notably higher in the deceased cohort (28.0% vs. 8.1%; p = 0.042). Although urgent percutaneous coronary intervention (PCI) was performed in the majority of cases, mortality remained significant. Conclusions: This study highlights that LVEF and hyperlipidemia are critical predictors of in-hospital mortality in patients with cardiogenic shock following STEMI. Other studies also suggest the prognostic value of LVEF in various cardiac conditions, particularly in the context of acute coronary syndromes [14, 15]. Also, the combination of renal dysfunction left ventricular ejection fraction, and advanced age has been proposed as a predictor of in-hospital mortality [18]. These findings underscore the importance of early identification and targeted management strategies to improve patient outcomes in this high-risk population.

The combined effect of bed-to-nurse ratio and nurse turnover rate on in-hospital mortality based on South Korean administrative data: a cross-sectional study

Background and aimNurse staffing levels are associated with patient mortality, but little is known regarding the association between nurse turnover rate and patient mortality. This study investigated the combined effect of the bed-to-nurse ratio and the nurse turnover rate on in-hospital mortality in patients admitted to Korean acute care hospitals using national administrative data.MethodsThis study analyzed data from the National Health Insurance Service (NHIS) on 459,113 admitted patients and 111,342 employed nurses in 403 hospitals in South Korea from January to December 2016. Differences in in-hospital mortality and nurse turnover among hospital characteristics, including the bed-to-nurse ratio, were explored using the chi-square test. Multilevel, multivariate GEE logistic regression analyses were used to examine the combined effect of the bed-to-nurse ratio and the nurse turnover rate on in-hospital mortality.ResultsDuring the study period, 13,675 (3.0%) patients died during hospitalization, and 13,349 (12.0%) nurses left their jobs. The risk of death among patients admitted to hospitals with a bed-to-nurse ratio of < 2.5 and a nurse turnover rate of ≥ 12% was lower than among patients admitted to hospitals with a bed-to-nurse ratio of ≥ 4.5 and a nurse turnover rate of ≥ 12% (odds ratio [OR] = 0.63; 95% confidence interval [CI], 0.48–0.82). The risk of in-hospital mortality decreased further when the nurse turnover rate was < 12% (OR = 0.59; 95% CI, 0.44–0.79).ConclusionThe bed-to-nurse ratio and nurse turnover rate were jointly associated with patient mortality. When hospitals with a low bed-to-nurse ratio also experienced high nurse turnover, the risk of in-hospital mortality was even greater. The finding of this study will help health policy makers to better understand the importance of both nursing staffing levels and nurse turnover rates. It is necessary to create a comprehensive improvement plan that integrates policies aiming to improve nurse staffing levels and reduce turnover rates into a single strategy.

P-1999. Predictors of Mortality in Myelodysplastic Syndrome with COVID-19: A 2020-2021 National Study

Abstract Background We aimed to evaluate mortality predictors in Myelodysplastic Syndrome who were admitted for COVID-19 (MDSCov) using the Healthcare Cost and Utilization Project. Sociodemographics in MDSCoV Patients Methods A cross-sectional study using the NIS on adults (≥ 18 years) hospitalized in the United States for MDSCov (n=769). We identified 151 nonsurvivors among MDSCov. Analysis was done using t-tests and chi-square tests, and multivariate regression models were constructed with p≤ 0.05 significance. Factors Associated with Higher Mortality in MDSCoV Patients Results The average age of MDSCov was higher than those without COVID-19 (77.3 vs 53.5 years, p=0.0000). MDSCov had a longer average length of stay (7.9 vs 5.1 days, p=0.0000) and higher total hospital charges ($85,059.6 vs $66887.8, p=0.0001) compared to MDS patients without COVID-19. We found that HSCT (allogenic and autogenic) was not associated with increased MDSCov hospitalization (2.2% vs 2.5%, p=0.8286). Males experienced a higher mortality rate (64.9% vs 53.9%, p=0.015; aOR 1.51, p=0.076) as they have higher expression of ACE2 receptors than females, correlating to disease severity. Race did not contribute to mortality (White 72.8% vs 77.3%, p=0.5267). The Non Survivors of MDSCov showed a high association with stroke (2% vs 0.3%, p=0.0227; aOR 4.7, p=0.026), severe sepsis (17.9% vs 0.6%, 0.0000; 9.1, 0.000), acute respiratory failure (66.9% vs 49.5%, 0.0001; 3.93, 0.000), and acute kidney injury (45.7% vs 29.1%, 0.0001; 1.27, 0.326). Due to COVID-19, T-cell exhaustion and secondary infections that contribute to sepsis can occur. Increased microthrombus formation and endothelial dysfunction can lead to thromboembolic episodes. Increased proinflammatory cytokines (IL-1β, IL-6, IL-12, IFN-γ, IP-10, and MCP-1) caused by the virus contribute to pulmonary inflammation and lung injury. The use of vasopressors to maintain MAP (9.3% vs 0.2%, p=0.0000; aOR 6.65, p=0.081) was markedly higher but not significant among nonsurvivors. Sociodemographic Profile of Patients with MDS Conclusion Our study identified several predictors of mortality in MDSCov. However, due to the need for more information on disease severity and laboratory evidence, future studies are needed to develop timely treatment plans. Appropriate chemotherapeutic regimens for MDS and antivirals/biologics for COVID-19 are needed. Predictors of In-hospital Mortality in Patients with MDS and COVID-19 Disclosures All Authors: No reported disclosures

P-876. Factors Influencing Outcome in Patients Admitted with Melioidosis- A Single Centre Retrospective Study

Abstract Background Melioidosis is largely under-reported in the Indian subcontinent. This region is believed to account for 44% of total cases, with an estimated annual incidence of 52,500 cases. This retrospective study of 50 patients aimed at determining association between factors like diabetes mellitus, hypertension, existing heart disease, lung disease, liver disease, alcohol abuse, immunosuppression, symptoms at admission to the hospital, and time from symptom onset to isolation of the bacterium, on in-hospital mortality, development of acute kidney injury (AKI), need for renal replacement therapy (RRT), intensive care unit (ICU) stay and recurrence. Objectives To study the risk factors influencing in-hospital mortality in patients admitted with melioidosis. To study the association between risk factors identified and developemenet of Acute Kidney Injury, the need for Renal Replacement Therapy, ICU stay and recurrence of the disease. Methods Study setting: Teaching hospitals affiliated with Kasturba Medical College, Mangalore Study design: Retrospective Cross Sectional Study Inclusion Criteria: Patients admitted and diagnosed with Melioidosis, Age &amp;gt;18 years Exclusion Criteria: Pre-existing kidney disease Results Data analysis showed that diabetes, hypertension, pre-existing heart disease, lung disease, liver disease, alcohol abuse, immunosuppression, symptom complex, time from symptom onset to isolation of organism, and development of AKI had no significant associations with in-hospital mortality. Hypertension, pre-existing heart disease, hyponatremia, and advanced age was noted to have statistically significant associations with development of AKI. Hypertension, pre-existing heart disease, and advanced age was noted have statistically significant associations with need for RRT. Pre-existing heart disease, and longer ICU stay had statistically significant associations with recurrence of the disease. Further analysis, with respect to geographic influences is ongoing. Conclusion In patients with Melioidosis, hypertension, pre-existing cardiac disease, hyponatremia, and advanced age were noted to have significant association with development of AKI while pre-existing cardiac disease and longer ICU stay were associated with recurrence. Disclosures All Authors: No reported disclosures

Should the Start of Immunosuppressive Treatment for COVID-19 Rely upon the Degree of Inflammation or the Time from Onset?

Background and Objectives: A COVID-19 model with a viral first-week phase and an inflammatory second phase has been proposed. It has been suggested that immunosuppressive treatment in the first week is harmful. This study aimed to analyze the potential damage of corticosteroids (CS) administered in the first week of COVID-19. Materials and Methods: This study was performed on a large cohort of consecutive COVID-19 patients admitted to Bellvitge University Hospital (Barcelona, Spain) from March 2020 to April 2021. Patients diagnosed with COVID-19 who were treated with 6 mg of dexamethasone a day for 10 days, and whose initiation of administration occurred within the first 2 weeks from symptom onset were included. We divided the cohort into the following two groups: patients for whom CS were initiated within the first 7 days after symptom onset vs. patients for whom CS were initiated between days 8 and 14. The degree of analytical inflammation (based on lymphocyte count, C-reactive protein, ferritin, lactate dehydrogenase, and D-dimer) upon admission was taken into account. The primary outcome was in-hospital mortality. Results: A total of 581 patients met the inclusion criteria. The results included, as follows: differences in age at baseline between groups (70.8 years old vs. 62.7, p &lt; 0.001); moderate-to-severe dependency (11.9% vs. 4.2%, p = 0.003); the lymphocyte count (840 × 106/L vs. 900, p = 0.033); D-dimer (400 ng/mL vs. 309, p &lt; 0.001); and PaO2/FiO2 (290 vs. 311, p &lt; 0.001). In-hospital mortality in patients who received CS in the first week of symptom onset was higher (29% vs. 12.8%, p &lt; 0.001). The following risk factors were associated with higher in-hospital mortality: age (OR = 1.06, p &lt; 0.001); Charlson index (OR = 1.34, p = 0.001); tachypnea &gt; 20 bpm (OR = 2.58, p &lt; 0.001); ≥3 high-risk criteria of inflammation (OR = 1.94, p = 0.012); and CS onset in the first week (OR = 2.17, p = 0.004). A higher PaO2/FiO2 (OR = 0.99, p &lt; 0.001) and the use of remdesivir (OR = 0.53, p = 0.021) were identified as protective factors. However, when stratified by analytical inflammation criteria, the onset of CS in the first week did not reach statistical significance. Conclusions: The early administration of CS did not demonstrate a significant detrimental effect. These results highlight the need for a nuanced approach to CS therapy in COVID-19 that carefully weighs the risks and benefits based on individual patient characteristics and the severity of the inflammation.

Impact of adherence to quality indicators and effects of targeted treatment with cefazolin or flucloxacillin on in-hospital mortality in patients with methicillin-susceptible Staphylococcus aureus (MSSA) bloodstream infections: a retrospective observational study.

To analyze the associations between adherence to quality indicators (QIs) in the treatment of bloodstream infections caused by methicillin-susceptible Staphylococcus (S.) aureus (MSSA) and in-hospital mortality. A retrospective observational study was conducted in patients admitted between 2019 and 2023 to Hospital St. Georg in Leipzig, Germany, with at least one positive blood culture for S. aureus. Ten QIs were categorized into four groups based on blood culture results, echocardiography, antibiotic treatment, and other parameters such as infectious disease (ID) specialist consultation. Propensity score (PS) matching was used to compare in-hospital mortality between MSSA patients treated with flucloxacillin and those treated with cefazolin. Multivariate Cox regression analysis was performed to determine risk factors associated with in-hospital mortality. Of the 637 patients with S. aureus bloodstream infections, 495 patients with MSSA infection (77.8%) were included in the study. After the introduction of mandatory ID consultation in 2020, the median QI score increased to 9 out of a maximum of 10 points and was significantly higher in surviving cases than in fatal cases in subsequent years. There was a non-significant decrease in in-hospital mortality from 2019 (28.8%) to 2023 (22.7%) (p = 0.432). Based on PS matching, cefazolin had a favorable hazard ratio of 0.44 (95% CI 0.28-0.71; p < 0.001) for in-hospital mortality. The results of multivariate Cox regression analysis showed a significantly higher survival rate in patients who received QI-based management, including transesophageal echocardiography and antibiotic therapy initiated within 24h. ID consultation is associated with better adherence to quality improvement measures. Targeted MSSA therapy with cefazolin, early initiation of antibiotic therapy, and adherence to antimicrobial treatment protocols increased survival rates in our study setting.

Lactate-enhanced-qSOFA (LqSOFA) score as a predictor of in-hospital mortality in patients with sepsis: systematic review and meta-analysis.

Sepsis is a systemic process that refers to a deregulated immune response of the host against an infectious agent, involving multiple organ dysfunction. It is rapidly progressive and has a dismal prognosis, with high mortality rates. For this reason, it is necessary to have a tool for early recognition of these patients, with the aim of treating them appropriately in a timely manner. This research is a systematic review based on bibliography indexed in four online scientific databases for studies published since inception to February 2024, which was obtained through the use of a search strategy. Eight studies were identified for quantitative analysis and included in our meta-analysis. The meta-analysis revealed that among 23,551 patients diagnosed with sepsis, 5,825 had a positive LqSOFA, and 3,086 experienced the primary outcome (mortality). For LqSOFA, a sensitivity of 0.61 (95% CI 0.60-0.63), specificity of 0.81 (95% CI 0.80-0.81), positive likelihood ratio (LR+) of 3.46 (95% CI 2.86-4.18), negative likelihood ratio (LR-) of 0.47 (95% CI 0.38-0.59), and odds ratio (OR) of 7.43 (95% CI 6.01-9.20) were determined. The area under the curve (AUC) was 0.807. The LqSOFA score demonstrates a good predictive capacity for in-hospital mortality in septic patients, showing clinically significant levels of sensitivity (69%) and specificity (79%).

Predictive strength of inflammatory scores for in-hospital mortality in infective endocarditis.

Inflammatory markers have been proposed as prognostic tools for predicting in-hospital mortality in infective endocarditis (IE). Nonetheless, it is unclear whether these markers provide additional prognostic value over established indicators. This study compared nine different inflammation scores to assess their effectiveness in enhancing the prediction of in-hospital mortality. Patients with IE diagnosed between 2017 and 2023 at two cardiology centers in Istanbul were included in this study. Pre-treatment inflammatory markers were obtained from the hospital electronic database system. In-hospital mortality prognostication was assessed using Cox proportional hazards models. Atotal of 122 patients who were diagnosed with IE were included in the analysis. Overall, 38patients died during the hospital stay. The patients were categorized into two groups based on their mortality status. The prognostic nutritional index (PNI), platelet-to-lymphocyte ratio (PLR), and modified Glasgow prognostic score (mGPS) were identified as statistically significant predictors of in-hospital mortality. Based on the results of Cox regression analysis, the PNI (hazard ratio [HR]: 0.921, 95%confidence interval [CI]: 0.853-0.994, p = 0.035) emerged as the only independent predictor of in-hospital mortality of IE patients. Nine inflammatory scores were evaluated in this study. The PNI, PLR, and mGPS were statistically significant predictors of in-hospital mortality in patients with IE. The PNI was identified as the optimal score.

Ph Level Analysis Study on Patient Mortality in Hospitals

Chronic diseases are significant health issues classified by WHO into communicable and non-communicable types. Acid-base balance disorders are suspected to exacerbate disease severity, increase mortality risk, and potentially result in death. This research aimed to investigate the relationship between blood pH levels and mortality among hospitalized patients. A cross-sectional observational analysis was employed using medical record data from patients diagnosed with heart disease (CHF, IHD), diabetes mellitus (DM), stroke (ischemic and hemorrhagic), pulmonary disorders (pneumonia, tuberculosis), kidney function disorders (CKD), and sepsis (septic shock). Arterial blood gas analysis (ABG) data were collected from Jogja Hospital patients during 2022-2023. The research involved 194 subjects, categorized into two groups: deceased and surviving patients. Statistical analysis using a t-test compared the average pH levels between the groups. The findings revealed that the average pH level in the living group was 7.325, whereas the deceased group had a more acidic pH of 7.25, with a statistically significant difference (p&lt;0.05). Severe acidosis, often caused by hyperlactatemia, was associated with organ dysfunction and increased mortality risk, particularly among sepsis patients. The research highlights that acidic blood pH, indicative of acidosis, is linked to various etiologies and a poor prognosis. These results underscore the importance of monitoring and managing acid-base balance disorders to improve patient outcomes and reduce mortality.

Machine learning prediction of in-hospital mortality and external validation in patients with cardiogenic shock: the RESCUE score.

Despite advances in mechanical circulatory support, mortality rates in cardiogenic shock (CS) remain high. A reliable risk stratification system could serve as a valuable guide in the clinical management of patients with CS. This study aimed to develop and externally validate a risk prediction model for in-hospital mortality in CS patients using machine learning (ML) algorithms. Data from 1247 patients with all-cause CS in the RESCUE registry (January 2014-December 2018) were analyzed. Key predictive variables were identified using 4 ML algorithms. A risk prediction model, the RESCUE score, was developed using logistic regression based on the selected variables. Internal validation was conducted within the RESCUE registry, and external validation was performed using an independent CS registry of 750 patients. The 4 ML models identified 7 predictors: age, vasoactive inotropic score, left ventricular ejection fraction, lactic acid level, in-hospital cardiac arrest at presentation, need for continuous renal replacement therapy, and mechanical ventilation. The RESCUE score demonstrated strong predictive performance, with an AUC of 0.86 (95%CI, 0.83-0.88) for in-hospital mortality. Ten-fold internal cross-validation yielded an AUC of 0.86 (95%CI, 0.77-0.95). External validation showed an AUC of 0.80 (95%CI, 0.76-0.84). Our ML-based risk-scoring system, the RESCUE score, demonstrated excellent predictive performance for in-hospital mortality in all patients with CS patients, regardless of cause. The system could be a useful and reliable tool to estimate risk stratification of CS in everyday clinical practice. NCT02985008.

Evaluation of the predictive value of the glucose-to-potassium ratio in predicting in-hospital mortality of patients with sepsis and septic shock.

In this study, we aimed to evaluate the role of the glucose-to-potassium ratio in predicting in-hospital mortality and prognosis of patients diagnosed with sepsis and septic shock in an emergency department and admitted to an emergency critical intensive care unit (ICU). This study was aretrospective and observational evaluation of nontraumatic sepsis and septic shock patients > 18years of age who were admitted to the emergency department of atertiary training and research hospital and had available glucose and potassium values at the time of admission. The patients were evaluated over a24-month period. The primary goal of this study was to examine the relationship between the glucose-to-potassium ratio and in-hospital mortality in sepsis patients. Data derived from 175 patients were included in the statistical analysis. Blood urea nitrogen, creatinine, lactate dehydrogenase, direct bilirubin, C‑reactive protein, and lactate levels were found to be significantly higher in the nonsurvivor group than in the survivor group (p < 0.05). On the other hand, hemoglobin, hematocrit, albumin, pH, HCO3 and base excess levels were found to be statistically significantly higher in the survivor group than in the nonsurvivor group (p < 0.05). The glucose-to-potassium ratio was not significant in terms of predicting mortality in sepsis patients (p = 0.324). In the receiver-operating characteristic (ROC) analysis of various parameters' significance in terms of predicting mortality, APACHE2 scores had the highest area under the curve (AUC) value (0.729). According to the results of this study, the glucose-to-potassium ratio did not have asignificant value in predicting mortality risk in sepsis and septic shock patients admitted from the emergency department to the emergency critical ICU.

Neutrophil to platelet ratio predicts in-hospital mortality in patients with acute myocardial infarction.

Acute myocardial infarction (AMI) is a critical medical emergency worldwide and a leading cause of mortality. This study aims to investigate the predictive utility of the neutrophil-to-platelet ratio (NPR) in identifying AMI patients at an increased risk of in-hospital mortality. We enrolled 664 patients, including 421 with ST-elevation myocardial infarction (STEMI) and 243 with non-ST-elevation myocardial infarction (NSTEMI), at Zhongshan Hospital, Fudan University, from January 2020 to September 2023. NPR was calculated as the neutrophil count divided by the platelet count. The primary outcome was defined as in-hospital mortality. The overall in-hospital mortality among AMI patients was 6.78%. Mortality was notably higher in the high NPR group compared to the low NPR group. Univariate analysis identified several variables significantly associated with in-hospital mortality, including age, left ventricular ejection fraction (LVEF), neutrophil-to-lymphocyte ratio (NLR), and NPR. NPR demonstrated a strong independent association with in-hospital mortality following adjustment for potential confounders. Receiver operating characteristic (ROC) curve analyses were performed to assess the discriminative power of NPR and NLR in predicting in-hospital mortality. NPR exhibited an area under the curve (AUC) of 0.755 (95% CI, 0.682-0.829, p < 0.001), indicating good discriminative ability. Similarly, NLR showed a discriminative AUC of 0.674 (95% CI, 0.586-0.762, p < 0.001). The optimal cutoff values for predicting mortality were determined as 0.042 for NPR (sensitivity 80%, specificity 62.2%) and 8.02 for NLR (sensitivity 62.2%, specificity 67.5%). Bootstrap validation with 1000 iterations confirmed the robustness of these findings, with validated AUCs of 0.755 (95% CI, 0.681-0.826) for NPR and 0.674 (95% CI, 0.587-0.766) for NLR. This study identifies NPR as an independent and valuable predictor of in-hospital mortality among AMI patients, The findings underscore NPR's potential utility in clinical practice for risk stratification and early intervention strategies aimed at reducing mortality rates in this high-risk patient population.

In-hospital mortality of patients admitted to the intermediate care unit in hospitals with and without an intensive care unit: a nationwide inpatient database study

BackgroundIntermediate care units (IMCUs) provide care for patients who need more intensive treatment than general wards but less than intensive care units (ICUs). Although the concept of an IMCU requires co-location with an ICU, some hospitals have IMCUs but no ICUs, which potentially worsens patient outcomes. This study aimed to examine the annual trends and care processes, and compare the outcomes of patients admitted to IMCUs in hospitals with and without ICUs using a nationwide inpatient database in Japan.MethodsThis retrospective cohort study was conducted from 2016 to 2022 using the Diagnosis Procedure Combination Database and Hospital Bed Function Reports in Japan. The main exposure was admission to the IMCU in hospitals with and without ICUs. The primary outcome was in-hospital mortality rate in hospitals with and without ICUs that were compared using multilevel logistic regression models adjusted for confounders.ResultsThe number of IMCU beds in hospitals without ICUs increased by 59% from 3,388 in 2016 to 5,403 in 2022, and the IMCU beds in hospitals without ICUs represented 38% (n = 5,403/14,185) of all IMCU beds in Japan in 2022. Among the 3,061,960 eligible patients in the IMCUs, 2,296,939 (75%) and 765,021 (25%) were admitted to hospitals with and without ICUs, respectively. Transfer between IMCUs and ICUs occurred for 10.5% (n = 320,938/3,061,960) of patients, with a large variability between hospitals. After adjusting for potential confounders, patients admitted to IMCUs in hospitals without ICUs had significantly higher in-hospital mortality than those in hospitals with ICUs (adjusted odds ratio: 1.15; 95% confidence interval: 1.10–1.20; p < 0.001).ConclusionsAdmission in IMCUs in hospitals without ICUs increased, but was associated with higher in-hospital mortality. These findings suggest that IMCUs should be placed in hospitals with ICUs.

Reverse shock index multiplied by Glasgow coma scale (rSIG) to predict mortality in traumatic brain injury: systematic review and meta-analysis.

To determine whether the Reverse Shock Index multiplied by the Glasgow Coma Scale (rSIG) is a predictor of in-hospital mortality in patients with traumatic brain injury (TBI). This is a systematic review and meta-analysis. A comprehensive search was conducted in five databases for studies published up to May 22, 2024, using a PECO strategy. Eight studies were identified for quantitative analysis and included in our meta-analysis. The participants of the included primary studies. Patients with a low rSIG as a predictor of in-hospital mortality in TBI. rSIG, in-hospital mortality, TBI. Our meta-analysis evaluated a total of eight observational studies encompassing 430,000 patients with TBI, observing 6,417 deaths (15%). After performing a sensitivity analysis, we found that patients with TBI and a low value of the reverse shock index multiplied by the Glasgow Coma Scale (rSIG) had a 24% higher risk of death (OR 1.24; 95% CI 1.12-1.38; I²: 96%). Furthermore, rSIG values were significantly higher in survivors compared to those who died (MD 7.72; 95% CI 1.86-13.58; I²: 99%).

Age-specific prognosis of acute and steady minor elevations in cardiac troponin among non-acute myocardial infarction inpatients: a large real-world cohort study

BackgroundThe upper reference limit of normal (ULN) of cardiac troponin (cTn) for older adults can be higher than for young adults, while the same ULN is used for both older...

Predicting Mortality in Patients Hospitalized With Acute Myocardial Infarction: From the National Cardiovascular Data Registry.

In-hospital mortality risk prediction is an important tool for benchmarking quality and patient prognostication. Given changes in patient characteristics and treatments over time, a contemporary risk model for patients with acute myocardial infarction (MI) is needed. Data from 313 825 acute MI hospitalizations between January 2019 and December 2020 for adults aged ≥18 years at 784 sites in the National Cardiovascular Data Registry Chest Pain-MI Registry were used to develop a risk-standardized model to predict in-hospital mortality. The sample was randomly divided into 70% development (n=220 014) and 30% validation (n=93 811) samples, and 23 separate registry-based patient characteristics at presentation were considered for model inclusion using stepwise logistic regression with 1000 bootstrapped samples. A simplified risk score was also developed for individual risk stratification. The mean age of the study cohort was 65.3 (SD 13.1) years, and 33.6% were women. The overall in-hospital mortality rate was 5.0% (n=15 822 deaths). The final model included 14 variables, with out-of-hospital cardiac arrest, cardiogenic shock, and ST-segment elevation MI as the strongest independent predictors of mortality. The model also included age, comorbidities (dyslipidemia, diabetes, prior percutaneous coronary intervention, cerebrovascular disease, and peripheral artery disease), heart failure on admission, heart rate, systolic blood pressure, glomerular filtration rate, and hemoglobin. The model demonstrated excellent discrimination (C-statistic, 0.868 [95% CI 0.865-0.871]) and good calibration, with similar performance across subgroups based on MI type, periods before and during the COVID-19 pandemic, and hospital volume. The simplified risk score included values from 0 to 25, with mortality risk ranging from 0.3% with a score of 0 to 1 up to 49.4% with a score >11. This contemporary risk model accurately predicts in-hospital mortality for patients with acute MI and can be used for risk standardization across hospitals and at the bedside for patient prognostication.

Acute upper gastrointestinal bleeding: Analysis of 462 hospitalized Chinese patients in Shanghai.

While acute upper gastrointestinal bleeding (AUGIB) remains clinically critical, the etiology of bleeding and risk factors for mortality remain uncertain. This study aimed to evaluate the underlying causes of AUGIB and identify risk factors associated with fatality. A retrospective survey was conducted in a major clinical hospital in Shanghai, where inpatients diagnosed with AUGIB were meticulously collected and analyzed. This retrospective, single-center study encompassed 462 patients diagnosed with AUGIB from the emergency department of Changhai Hospital between January 1, 2019 to November 30, 2021. The analysis involved various aspects including epidemiology, gastroscopy, imaging, laboratory tests and data, clinical features, and hospital mortality. Among the 462 patients studied, 87% (400 out of 462) were afflicted with noncirrhotic hemorrhage (up to 38% with peptic ulcer), and 4 patients required digital subtraction angiography embolization (two died). The mortality rate was 12% (49/400), of which 49% (24/49) of patients died of malignant tumors. Mortality did not differ according to sex (χ2 = 0. 851, P = .356) but was significantly influenced by age (χ2 = 18.292, P = .001), hemoglobin levels (χ2 = 22.908, P = .001), and comorbidity (χ2 = 17.355, P = .001). In patients with esophageal and gastric variceal bleeding, the mortality rate was as high as 25% (16/62). Their mortality did not vary with sex (χ2 = 0.318, P = .573), hemoglobin levels (χ2 = 4.139, P = .247), or age (χ2 = 7.674, P = .087), but was predominantly associated with hemorrhagic shock (88%, 14/16). Esophageal and gastric variceal bleeding and noncirrhotic hemorrhage patients observed significantly different mortality rates (P = .004). Peptic ulcers remained the primary cause of AUGIB. Old age, low hemoglobin levels, and comorbidities were the main risk factors contributing to mortality; nearly half of the patients died from malignant tumors. Hemorrhagic shock was independently associated with a high risk of in-hospital mortality in patients with cirrhotic hemorrhage.

Predicting In-Hospital Mortality in Patients With End-Stage Renal Disease Receiving Extracorporeal Membrane Oxygenation Therapy.

Patients on extracorporeal membrane oxygenation (ECMO) often experience worse renal outcomes and higher mortality rates as the severity of kidney injury increases. Nevertheless, the in-hospital mortality risks of patients with end-stage renal disease (ESRD) are poorly understood. This study evaluated several prognostic factors associated with in-hospital mortality in patients with ESRD receiving ECMO therapy. This study reviewed the medical records of 90 adult patients with ESRD on venoarterial ECMO in intensive care units in Linkou Chang Gung Memorial Hospital between March 2009 and February 2022. Fourteen patients who died within 24 hours of receiving ECMO support were excluded; the remaining 76 patients were enrolled. Demographic, clinical and laboratory variables were retrospectively collected as survival predictors. The primary outcome was in-hospital mortality. The overall in-hospital mortality rate was 69.7%. The most common diagnosis requiring ECMO support was postcardiotomy cardiogenic shock, and the most frequent ECMO-associated complication was infection. Multiple logistic regression analysis revealed that the Acute Physiology and Chronic Health Evaluation II (APACHE II) score on day 1 of ECMO support was an independent risk factor for in-hospital mortality. The APACHE II score demonstrated satisfactory discriminative power (0.788 ± 0.057) in the area under the receiver operating characteristic curve. The cumulative survival rates at the 6-month follow-up differed significantly (P < 0.001) between patients with APACHE II score ≤ 29 versus those with APACHE II score > 29. For patients with ESRD on ECMO, the APACHE II score is an excellent predictor of in-hospital mortality.

Construction and validation of a personalized risk prediction model for in-hospital mortality in patients with acute myocardial infarction undergoing percutaneous coronary intervention.

Although emergency Percutaneous Coronary Intervention (PCI) has been shown to reduce mortality in patients with Acute Myocardial Infarction (AMI), the risk of in-hospital death remains high. In this study, the authors aimed to identify risk factors associated with in-hospital mortality in AMI patients who underwent PCI, develop a nomogram prediction model, and evaluate its effectiveness. The authors retrospectively analyzed data from 1260 patients who underwent emergency PCI at Dongyang People's Hospital between June 1, 2013, and December 31, 2021. Patients were divided into two groups based on in-hospital mortality: the death group (n = 61) and the survival group (n = 1199). Clinical data between the two groups were compared. The Least Absolute Shrinkage and Selection Operator (LASSO) regression was used to select non-zero coefficients of predictive factors. Multivariable logistic regression analysis was then performed to identify independent risk factors for in-hospital mortality in AMI patients after emergency PCI. A nomogram model for predicting the risk of in-hospital mortality in AMI patients after PCI was constructed, and its predictive performance was evaluated using the c-index. Internal validation was performed using the bootstrap method with 1000 resamples. The Hosmer-Lemeshow test was used to assess the goodness of fit, and a calibration curve was plotted to evaluate the model's calibration. LASSO regression identified d-dimer, B-type natriuretic peptide, white blood cell count, heart rate, aspartate aminotransferase, systolic blood pressure, and the presence of postoperative respiratory failure as important predictive factors for in-hospital mortality in AMI patients after PCI. Multivariable logistic regression analysis showed that d-dimer, B-type natriuretic peptide, white blood cell count, systolic blood pressure, and the presence of postoperative respiratory failure were independent factors for in-hospital mortality. A nomogram model for predicting the risk of in-hospital mortality in AMI patients after PCI was constructed using these independent predictive factors. The Hosmer-Lemeshow test yielded a Chi-Square value of 9.43 (p = 0.331), indicating a good fit for the model, and the calibration curve closely approximated the ideal model. The c-index for internal validation was 0.700 (0.560‒0.834), further confirming the predictive performance of the model. Clinical decision analysis demonstrated that the nomogram model had good clinical utility, with an area under the ROC curve of 0.944 (95 % CI 0.903‒0.963), indicating excellent discriminative ability. This study identified B-type natriuretic peptide, white blood cell count, systolic blood pressure, d-dimer, and the presence of respiratory failure as independent factors for in-hospital mortality in AMI patients undergoing emergency PCI. The nomogram model based on these factors showed high predictive accuracy and feasibility.