Associations between static and dynamic changes of platelet counts and in-hospital mortality in critical patients with acute heart failure.

To investigate the predictive value of baseline platelet count and its short-term dynamic changes in the prognosis of patients with acute heart failure (AHF) in the intensive care unit. Patients diagnosed with AHF in the medical information mart for intensive care III and their clinical data were retrospectively filtered. Patients were divided into survivor and non-survivor groups based on their prognosis during hospitalization, and differences in baseline data between groups were compared. Logistic regression models and restricted cubic spline (RCS) plots were performed to evaluate the relationship between baseline platelet counts and in-hospital mortality. Changes and trends in platelet counts were compared between the survivor and non-survivor groups after adjusting for confounders with the generalized additive mixing model (GAMM). A total of 2930 critical patients with acute heart failure were included, of which 2720 were survivors and 210 were non-survivors. Multiple logistic regression models revealed that baseline platelet count was an independent factor in hospital mortality (OR 0.997, 95% CI 0.994-0.999, P-value = 0.018). The RCS plot demonstrated a U-shaped dose-response relationship between baseline platelet count and in-hospital mortality. GAMM analysis suggested that the platelet counts decreased and then increased in the survivor group and gradually decreased in the non-survivor group, with a gradual increase of difference between two groups. After adjusting for confounders, the mean daily increase was -6.014 (95% CI -7.076-4.953, P-value < 0.001). Baseline platelet demonstrated a U-shaped dose-response relationship with adverse outcomes in critical patients with AHF. Early elevation of platelet was correlated with higher in-hospital mortality, indicating that tracking early changes in platelet might help determine the short-term prognosis of critical patients with AHF.

Risk factors of in-hospital mortality and discriminating capacity of NIVO score in exacerbations of COPD requiring noninvasive ventilation.

BACKGROUND: Noninvasive mechanical ventilation (NIV) is recommended as the initial mode of ventilation to treat acute respiratory failure in patients with AECOPD. The Noninvasive Ventilation Outcomes (NIVO) score has been proposed to evaluate the prognosis in patients with AECOPD requiring assisted NIV. However, it is not validated in Chinese patients. METHODS: We used data from the MAGNET AECOPD Registry study, which is a prospective, noninterventional, multicenter, real-world study conducted between September 2017 and July 2021 in China. Data for the potential risk factors of mortality were collected and the NIVO score was calculated, and the in-hospital mortality was evaluated using the NIVO risk score. RESULTS: A total of 1164 patients were included in the study, and 57 patients (4.9%) died during their hospital stay. Multiple logistic regression analysis revealed that age ≥75 years, DBP <60 mmHg, Glasgow Coma Scale ≤14, anemia and BUN >7 mmol/L were independent predictors of in-hospital mortality. The in-hospital mortality was associated with an increase in the risk level of NIVO score and the difference was statistically significant (p < .001). The NIVO risk score showed an acceptable accuracy for predicting the in-hospital mortality in AECOPD requiring assisted NIV (AUC: 0.657, 95% CI: 0.584-0.729, p < .001). CONCLUSION: Our findings identified predictors of mortality in patients with AECOPD receiving NIV, providing useful information to identify severe patients and guide the management of AECOPD. The NIVO score showed an acceptable predictive value for AECOPD receiving NIV in Chinese patients, and additional studies are needed to develop and validate predictive scores based on specific populations.

Length of Hospitalization and Mortality among Stroke Patients before and after the Implementation of a Specialized Unit: A Retrospective Cohort Study Using Real-World Data from One Reference Hospital in Southern Brazil.

Stroke constitutes a significant global cause of mortality and disability. The implementation of stroke units influences hospital quality indicators, guiding care management. We aimed to compare hospital length of stay (LOS), in-hospital mortality, and post-discharge mortality between stroke patients admitted in the pre- and post-implementation periods of a stroke unit in a public hospital in southern Brazil. This retrospective cohort study used real-world data from one reference hospital, focusing on the intervention (stroke unit) and comparing it to the general ward (control). We analyzed the electronic medical records of 674 patients admitted from 2009 to 2012 in the general ward and 766 patients from 2013 to 2018 in the stroke unit. Admission to the stroke unit was associated with a 43% reduction in the likelihood of prolonged hospitalization. However, there was no significant difference in the risk of in-hospital mortality between the groups (Hazard ratio = 0.90; Interquartile range = 0.58 to 1.39). The incidence of death at three, six and twelve months post-discharge did not differ between the groups. Our study results indicate significant improvements in care processes for SU patients, including shorter LOS and better adherence to treatment protocols. However, our observations revealed no significant difference in mortality rates, either during hospitalization or after discharge, between the SU and GW groups. While SU implementation enhances efficiency in stroke care, further research is needed to explore long-term outcomes and optimize management strategies.

Development and validation of novel simple prognostic model for predicting mortality in Korean intensive care units using national insurance claims data.

Background/Aims: Intensive care unit (ICU) quality is largely determined by the mortality rate. Therefore, we aimed to develop and validate a novel prognostic model for predicting mortality in Korean ICUs, using national insurance claims data. Methods: Data were obtained from the health insurance claims database maintained by the Health Insurance Review and Assessment Service of South Korea. From patients who underwent the third ICU adequacy evaluation, 42,489 cases were enrolled and randomly divided into the derivation and validation cohorts. Using the models derived from the derivation cohort, we analyzed whether they accurately predicted death in the validation cohort. The models were verified using data from one general and two tertiary hospitals. Results: Two severity correction models were created from the derivation cohort data, by applying variables selected through statistical analysis, through clinical consensus, and from performing multiple logistic regression analysis. Model 1 included six categorical variables (age, sex, Charlson comorbidity index, ventilator use, hemodialysis or continuous renal replacement therapy, and vasopressor use). Model 2 additionally included presence/absence of ICU specialists and nursing grades. In external validation, the performance of models 1 and 2 for predicting in-hospital and ICU mortality was not inferior to that of pre-existing scoring systems. Conclusions: The novel and simple models could predict in-hospital and ICU mortality and were not inferior compared to the pre-existing scoring systems.

Effect of mortality prediction models on resource use benchmarking of intensive care units.

PURPOSE: Intensive care requires extensive resources. The ICUs' resource use can be compared using standardized resource use ratios (SRURs). We assessed the effect of mortality prediction models on the SRURs. MATERIALS AND METHODS: We compared SRURs using different mortality prediction models: the recent Finnish Intensive Care Consortium (FICC) model and the SAPS-II model (n = 68,914 admissions). We allocated the resources to severity of illness strata using deciles of predicted mortality. In each risk and year stratum, we calculated the expected resource use per survivor from our modelling approaches using length of ICU stay and Therapeutic Intervention Scoring System (TISS) points. RESULTS: Resource use per survivor increased from one length of stay (LOS) day and around 50 TISS points in the first decile to 10 LOS-days and 450 TISS in the tenth decile for both risk scoring systems. The FICC model predicted mortality risk accurately whereas the SAPS-II grossly overestimated the risk of death. Despite this, SRURs were practically identical and consistent. CONCLUSIONS: SRURs provide a robust tool for benchmarking resource use within and between ICUs. SRURs can be used for benchmarking even if recently calibrated risk scores for the specific population are not available.

Use of inpatient palliative care in metastatic urethral cancer.

BACKGROUND: In metastatic urethral cancer, temporal trends, and patterns of inpatient palliative care (IPC) use are unknown. METHODS: Relying on the National Inpatient Sample (2006-2019), metastatic urethral cancer patients were stratified according to IPC use. Estimated annual percentage changes (EAPC) analyses and multivariable logistic regression models (LRM) for the prediction of IPC use were fitted. RESULTS: Of 1,106 metastatic urethral cancer patients, 199 (18%) received IPC. IPC use increased from 5.8 to 28.0% over time in the overall cohort (EAPC +9.8%; P < 0.001), from <12.5 to 35.1% (EAPC +11.2%; P < 0.001), and from <12.5 to 24.7% (EAPC +9.4%; P = 0.01) in respectively females and males. Lowest IPC rates were recorded in the Midwest (13.5%) vs. highest in the South (22.5%). IPC patients were more frequently female (44 vs. 37%), and more frequently exhibited bone metastases (45 vs. 34%). In multivariable LRM, female sex (multivariable odds ratio [OR] 1.46, 95% confidence interval [CI] 1.05-2.02; P = 0.02), and bone metastases (OR 1.46, 95%CI 1.02-2.10; P = 0.04) independently predicted higher IPC rates. Conversely, hospitalization in the Midwest (OR 0.53, 95%CI 0.31-0.91; P = 0.02), and in the Northeast (OR 0.48, 95%CI 0.28-0.82; P = 0.01) were both associated with lower IPC use than hospitalization in the West. CONCLUSION: IPC use in metastatic urethral cancer increased from a marginal rate of 5.8% to as high as 28%. Ideally, differences according to sex, metastatic site, and region should be addressed to improve IPC use rates.

Impact of Delirium Onset and Duration on Mortality in Patients With Cancer Admitted to the ICU.

BACKGROUND: Little is known on the effects of delirium onset and duration on outcome in critically ill patients with cancer. OBJECTIVES: To determine the impact of delirium onset and duration on intensive care unit (ICU) and hospital mortality and length of stay (LOS) in patients with cancer. METHODS: Of the 915 ICU patients admitted in 2018, 371 were included for analysis after excluding for terminal disease, <24-h ICU stay, lack of active cancer and delirium. Delirium was defined as early if onset was within 2 days of ICU admission, late if onset was on day 3 or later, short if duration was 2 days or less, and long if duration was 3 days or longer. Patients were placed into 4 combination groups: early-short, early-long, late-short, and late-long delirium. Multivariate analysis controlling for sex, age, metastatic disease, and predelirium hospital LOS was performed to determine ICU and hospital mortality and LOS. Exploratory analysis of long-term survival was also performed. Restricted cubic splines were performed to confirm the use of 2 days to distinguish between early versus late onset and short versus long duration. RESULTS: A total of 32.9% (n = 122) patients had early-short, 39.1% (n = 145) early-long, 16.2% (n = 60) late-short, and 11.9% (n = 44) late-long delirium. Late-long delirium was independently associated with increased ICU (OR 4.45, CI 1.92-10.30; P < .001) and hospital (OR 2.91, CI 1.37-6.19; P = .005) mortality and longer ICU (OR 1.97, CI 1.58-2.47; P < .001) LOS compared to early-short delirium. Early delirium had better overall survival at 18 months than late delirium. Long-term survival further improved when delirium duration was 2 days or less. Prediction heatmaps confirm the use of a 2-day cutoff. CONCLUSION: Late delirium, especially with long duration, significantly worsens outcome in ICU patients with cancer and should be considered a harbinger of poor overall condition.

Noncardiac Surgery in Patients with Left Ventricular Assist Devices: A 11-Year Institutional Experience.

INTRODUCTION: Limited options in the end-stage treatment of heart failure have led to increased use of left ventricular assist devices. For this reason, the rate of non-cardiac surgeries in patients with left ventricular assist devices is also increasing. Our study aims to analyze surgical rate, anesthesia management, and results by reviewing our 11-year experience with patients who underwent non-cardiac surgery receiving left ventricular assist devices support. METHODS: We retrospectively evaluated 57 patients who underwent non-cardiac surgery and 67 non-cardiac surgical procedures among 274 patients who applied between January 2011 and December 2022 and underwent left ventricular assist devices implantation with end-stage heart failure. RESULTS: Fifty (74.6%) patients with left ventricular assist devices admitted to the hospital for non-cardiac surgery were emergency interventions. The most common reasons for admission were general surgery (52.2%), driveline wound revision (22.3%), and neurological surgery (14.9%). This patient group has the highest in-hospital mortality rate (12.8%) and the highest rate of neurological surgery (8.7%). While 70% of the patients who underwent neurosurgery were taken to surgery urgently, the International Normalized Ratio values of these patients were between 3.5 and 4.5 at the time of admission to the emergency department. CONCLUSION: With a perioperative multidisciplinary approach, higher morbidity and mortality risks can be reduced during emergencies and major surgical procedures.

Association Between In-hospital Mortality and the Institutional Factors of Intensive Care Units with a Focus on the Intensivist- to-bed Ratio: A Retrospective Cohort Study.

Purpose: To elucidate the relationship between in-hospital mortality and the institutional factors of intensive care units (ICUs), with a focus on the intensivist-to-bed ratio. Methods: A retrospective cohort study was conducted using a Japanese ICU database, including adult patients admitted between April 1, 2020 and March 31, 2021. We used a multilevel logistic regression model to investigate the associations between in-hospital mortality and the following institutional factors: the intensivist-to-bed ratios on weekdays or over weekends/holidays, different work shifts, hospital-to-ICU-bed ratio, annual-ICU-admission-to-bed ratio, type of hospital, and the presence of other medical staff. Results: The study population comprised 46 503 patients admitted to 65 ICUs. The in-hospital mortality rate was 8.1%. The median numbers of ICU beds and intensivists were 12 (interquartile range [IQR] 8-14) and 4 (IQR 2-9), respectively. In-hospital mortality decreased significantly as the intensivist-to-bed ratio at 10 am on weekdays increased: the average contrast indicated a 20% (95% confidence interval [CI]: 1%-38%) reduction when the ratio increased from 0 to 0.5, and a 38% (95% CI: 9%-67%) reduction when the ratio increased from 0 to 1. The other institutional factors did not present a significant effect. Conclusions: The intensivist-to-bed ratio at 10 am on weekdays had a significant effect on in-hospital mortality. Further investigation is needed to understand the processes leading to improved outcomes.

Impact of the early phase of the COVID-19 pandemic on emergency department-to-intensive care unit admissions in Korea: an interrupted time-series analysis.

BACKGROUND: The coronavirus disease 2019 (COVID-19) pandemic resulted in significant disruptions to critical care systems globally. However, research on the impact of the COVID-19 pandemic on intensive care unit (ICU) admissions via the emergency department (ED) is limited. Therefore, this study evaluated the changes in the number of ED-to-ICU admissions and clinical outcomes in the periods before and during the pandemic. METHODS: We identified all adult patients admitted to the ICU through level 1 or 2 EDs in Korea between February 2018 and January 2021. February 2020 was considered the onset point of the COVID-19 pandemic. The monthly changes in the number of ED-to-ICU admissions and the in-hospital mortality rates before and during the COVID-19 pandemic were evaluated using interrupted time-series analysis. RESULTS: Among the 555,793 adult ED-to-ICU admissions, the number of ED-to-ICU admissions during the pandemic decreased compared to that before the pandemic (step change, 0.916; 95% confidence interval [CI] 0.869-0.966], although the trend did not attain statistical significance (slope change, 0.997; 95% CI 0.991-1.003). The proportion of patients who arrived by emergency medical services, those transferred from other hospitals, and those with injuries declined significantly among the number of ED-to-ICU admissions during the pandemic. The proportion of in-hospital deaths significantly increased during the pandemic (step change, 1.054; 95% CI 1.003-1.108); however, the trend did not attain statistical significance (slope change, 1.001; 95% CI 0.996-1.007). Mortality rates in patients with an ED length of stay of ≥ 6 h until admission to the ICU rose abruptly following the onset of the pandemic (step change, 1.169; 95% CI 1.021-1.339). CONCLUSIONS: The COVID-19 pandemic significantly affected ED-to-ICU admission and in-hospital mortality rates in Korea. This study's findings have important implications for healthcare providers and policymakers planning the management of future outbreaks of infectious diseases. Strategies are needed to address the challenges posed by pandemics and improve the outcomes in critically ill patients.

Hospital Outcomes in Patients Who Developed Acute Respiratory Distress Syndrome After Community-Acquired Pneumonia.

Purpose: To identify risk factors for and outcomes in acute respiratory distress syndrome (ARDS) in patients hospitalized with community-acquired pneumonia (CAP). Methods: This is a retrospective study using the Premier Healthcare Database between 2016 and 2020. Patients diagnosed with pneumonia, requiring mechanical ventilation (MV), antimicrobial therapy, and hospital admission ≥2 days were included. Multivariable regression models were used for outcomes including in-hospital mortality, hospital length of stay (LOS), intensive care unit (ICU) LOS, and days on MV. Results: 1924 (2.7%) of 72 107 patients with CAP developed ARDS. ARDS was associated with higher mortality (33.7% vs 18.9%; adjusted odds ratio 2.4; 95% confidence interval [CI] 2.16-2.66), longer hospital LOS (13 vs 9 days; adjusted incidence risk ratio (aIRR) 1.24; 95% CI 1.20-1.27), ICU LOS (9 vs 5 days; aIRR 1.51; 95% CI 1.46-1.56), more MV days (8 vs 5; aIRR 1.54; 95% CI 1.48-1.59), and increased hospitalization cost ($46 459 vs $29 441; aIRR 1.50; 95% CI 1.45-1.55). Conclusion: In CAP, ARDS was associated with worse in-patient outcomes in terms of mortality, LOS, and hospitalization cost. Future studies are needed to explore outcomes in patients with CAP with ARDS and explore risk factors for development of ARDS after CAP.

A COVID-19 specific multiparametric and ECG-based score for the prediction of in-hospital mortality: ELCOVID score.

We aimed to develop and validate a COVID-19 specific scoring system, also including some ECG features, to predict all-cause in-hospital mortality at admission. Patients were retrieved from the ELCOVID study (ClinicalTrials.gov identifier: NCT04367129), a prospective, multicenter Italian study enrolling COVID-19 patients between May to September 2020. For the model validation, we randomly selected two-thirds of participants to create a derivation dataset and we used the remaining one-third of participants as the validation set. Over the study period, 1014 hospitalized COVID-19 patients (mean age 74 years, 61% males) met the inclusion criteria and were included in this analysis. During a median follow-up of 12 (IQR 7-22) days, 359 (35%) patients died. Age (HR 2.25 [95%CI 1.72-2.94], p < 0.001), delirium (HR 2.03 [2.14-3.61], p = 0.012), platelets (HR 0.91 [0.83-0.98], p = 0.018), D-dimer level (HR 1.18 [1.01-1.31], p = 0.002), signs of right ventricular strain (RVS) (HR 1.47 [1.02-2.13], p = 0.039) and ECG signs of previous myocardial necrosis (HR 2.28 [1.23-4.21], p = 0.009) were independently associated to in-hospital all-cause mortality. The derived risk-scoring system, namely EL COVID score, showed a moderate discriminatory capacity and good calibration. A cut-off score of ≥ 4 had a sensitivity of 78.4% and 65.2% specificity in predicting all-cause in-hospital mortality. ELCOVID score represents a valid, reliable, sensitive, and inexpensive scoring system that can be used for the prognostication of COVID-19 patients at admission and may allow the earlier identification of patients having a higher mortality risk who may be benefit from more aggressive treatments and closer monitoring.

Heterogeneous effects of hospital competition on inpatient quality: an analysis of five common diseases in China.

BACKGROUND: Many countries has introduced pro-competition policies in the delivery of healthcare to improve medical quality, including China. With the increasing intensity of competition in China's healthcare market, there are rising concerns among policymakers about the impact of hospital competition on quality. This study investigated heterogeneous effects of hospital competition on inpatient quality. METHODS: We analyzed the inpatient discharge dataset and selected chronic obstructive pulmonary disease (COPD), ischemic stroke, pneumonia, hemorrhagic stroke, and acute myocardial infarction (AMI) as representative diseases. A total of 561,429 patients in Sichuan Province in 2017 and 2019 were included. The outcomes of interest were in-hospital mortality and 30-day unplanned readmissions. The Herfindahl-Hirschman Index was calculated using predicted patient flows to measure hospital competition. To address the spatial correlations of hospitals and the structure of the dataset, the multiple membership multiple classification model was employed for analysis. RESULTS: Amid intensifying competition in the hospital market, our study discerned no marked statistical variance in the risk of inpatient quality across most diseases examined. Amplified competition exhibited a positive correlation with heightened in-hospital mortality for both COPD and pneumonia patients. Elevated competition escalated the risk of 30-day unplanned readmissions for COPD patients, while inversely affecting the risk for AMI patients. CONCLUSIONS: There is the heterogeneous impact of hospital competition on quality across various diseases in China. Policymakers who intend to leverage hospital competition as a tool to enhance healthcare quality must be cognizant of the possible influences of it.

NSTEMI mortality and hospital outcomes in patients with atrial fibrillation: A propensity score-matched analysis.

Background: Atrial fibrillation (AF) is associated with increased cardiovascular mortality. Data regarding the relationship between coronary artery disease (CAD) and AF is mixed. It is uncertain if AF directly increases the risk for future coronary events and if such patients are appropriately evaluated for CAD. Methods: This cross-sectional study was performed on hospitalized patients with NSTEMI and concurrent AF in 2019 using the National Inpatient Sample. In-hospital mortality, rates of diagnostic cardiac angiography, percutaneous coronary intervention, ventricular tachycardiac (VT), ventricular fibrillation (VF), cardiogenic shock, cardiac arrest, length of stay (LOS), and total hospitalization charges were studied. Results: A total of 433,965 patients met inclusion criteria (169,725 females [39.1 %], 307,985 Caucasian [71 %], 51,570 African American [11.8 %], 37,265 Hispanic [8.6 %]; mean [SD] age, 67.9 [6.2] years). 86,200 (19.8 %) patients with NSTEMI had AF, including 32,775 (38 %) female patients before propensity matching. Patients with NSTEMI and AF had increased odds of mortality (adjusted Odds ratio, 1.32; CI, 1.21-1.43; p < 0.001). AF patients were less likely to undergo diagnostic coronary angiography and PCI and had higher odds of VT, VF, cardiogenic shock, cardiac arrest, tracheal intubation, mechanical ventilation, increased LOS, and higher hospital charges than those without AF. Conclusion: AF was independently associated with increased mortality and serious cardiac complications in patients admitted with NSTEMI.

An observational study on the impact of overcrowding towards door-to-antibiotic time among sepsis patients presented to emergency department of a tertiary academic hospital.

BACKGROUND: The latest Surviving Sepsis Campaign 2021 recommends early antibiotics administration. However, Emergency Department (ED) overcrowding can delay sepsis management. This study aimed to determine the effect of ED overcrowding towards the management and outcome of sepsis patients presented to ED. METHODS: This was an observational study conducted among sepsis patients presented to ED of a tertiary university hospital from 18th January 2021 until 28th February 2021. ED overcrowding status was determined using the National Emergency Department Overcrowding Score (NEDOCS) scoring system. Sepsis patients were identified using Sequential Organ Failure Assessment (SOFA) scores and their door-to-antibiotic time (DTA) were recorded. Patient outcomes were hospital length of stay (LOS) and in-hospital mortality. Statistical analysis was done using Statistical Package for Social Sciences (SPSS) version 26. P-value of less than 0.05 for a two-sided test was considered statistically significant. RESULTS: Total of 170 patients were recruited. Among them, 33 patients presented with septic shock and only 15% (n = 5) received antibiotics within one hour. Of 137 sepsis patients without shock, 58.4% (n = 80) received antibiotics within three hours. We found no significant association between ED overcrowding with DTA time (p = 0.989) and LOS (p = 0.403). However, in-hospital mortality increased two times during overcrowded ED (95% CI 1-4; p = 0.041). CONCLUSION: ED overcrowding has no significant impact on DTA and LOS which are crucial indicators of sepsis care quality but it increases overall mortality outcome. Further research is needed to explore other factors such as lack of resources, delay in initiating fluid resuscitation or vasopressor so as to improve sepsis patient care during ED overcrowding.

Lactate dehydrogenase to albumin ratio is associated with in-hospital mortality in patients with acute heart failure: Data from the MIMIC-III database.

The effect of the lactate dehydrogenase to albumin ratio (LAR) on the survival of patients with acute heart failure (AHF) is unclear. We aimed to analyze the impact of LAR on survival in patients with AHF. We retrieved eligible patients for our study from the Monitoring in Intensive Care Database III. For each patient in our study, we gathered clinical data and demographic information. We conducted multivariate logistic regression modeling and smooth curve fitting to assess whether the LAR score could be used as an independent indicator for predicting the prognosis of AHF patients. A total of 2,177 patients were extracted from the database. Survivors had an average age of 69.88, whereas nonsurvivors had an average age of 71.95. The survivor group had a mean LAR ratio of 13.44, and the nonsurvivor group had a value of 17.38. LAR and in-hospital mortality had a nearly linear correlation, according to smooth curve fitting (P < 0.001). According to multivariate logistic regression, the LAR may be an independent risk factor in predicting the prognosis of patients with AHF (odd ratio = 1.09; P < 0.001). The LAR ratio is an independent risk factor associated with increased in-hospital mortality rates in patients with AHF.

Contemporary Risk Models for In-Hospital and 30-Day Mortality After Percutaneous Coronary Intervention.

PURPOSE OF REVIEW: Risk models for mortality after percutaneous coronary intervention (PCI) are underutilized in clinical practice though they may be useful during informed consent, risk mitigation planning, and risk adjustment of hospital and operator outcomes. This review analyzed contemporary risk models for in-hospital and 30-day mortality after PCI. RECENT FINDINGS: We reviewed eight contemporary risk models. Age, sex, hemodynamic status, acute coronary syndrome type, heart failure, and kidney disease were consistently found to be independent risk factors for mortality. These models provided good discrimination (C-statistic 0.85-0.95) for both pre-catheterization and comprehensive risk models that included anatomic variables. There are several excellent models for PCI mortality risk prediction. Choice of the model will depend on the use case and population, though the CathPCI model should be the default for in-hospital mortality risk prediction in the United States. Future interventions should focus on the integration of risk prediction into clinical care.

Predicting outcomes of the acute phase of COVID-19. High sensitive prognostic model, based on the results of the international registry "analysis of chronic non-infectious diseases dynamics after COVID-19 infection in adult patients" (ACTIV).

The aim of this study is to investigate the course of the acute period of COVID-19 and devise a prognostic scale for patients hospitalized. Materials and methods: The ACTIV registry encompassed both male and female patients aged 18 years and above, who were diagnosed with COVID-19 and subsequently hospitalized. Between June 2020 and March 2021, a total of 9364 patients were enrolled across 26 medical centers in seven countries. Data collected during the patients' hospital stay were subjected to multivariate analysis within the R computational environment. A predictive mathematical model, utilizing the "Random Forest" machine learning algorithm, was established to assess the risk of reaching the endpoint (defined as in-hospital death from any cause). This model was constructed using a training subsample (70% of patients), and subsequently tested using a control subsample (30% of patients). Results: Out of the 9364 hospitalized COVID-19 patients, 545 (5.8%) died. Multivariate analysis resulted in the selection of eleven variables for the final model: minimum oxygen saturation, glomerular filtration rate, age, hemoglobin level, lymphocyte percentage, white blood cell count, platelet count, aspartate aminotransferase, glucose, heart rate, and respiratory rate. Receiver operating characteristic analysis yielded an area under the curve of 89.2%, a sensitivity of 86.2%, and a specificity of 76.0%. Utilizing the final model, a predictive equation and nomogram (termed the ACTIV scale) were devised for estimating in-hospital mortality amongst COVID-19 patients. Conclusion: The ACTIV scale provides a valuable tool for practicing clinicians to predict the risk of in-hospital death in patients hospitalized with COVID-19.

Development and validation of a predicting nomogram for in-hospital mortality of COVID-19 Omicron variant: A cohort study of 1324 cases in Beijing Anzhen Hospital.

Coronavirus disease 2019 (COVID-19) is continuously posing high global public health concerns due to its high morbidity and mortality. This study aimed to construct a convenient risk model for predicting in-hospital mortality of COVID-19 Omicron variant. A total of 1324 hospitalized patients with Omicron variant were enrolled from Beijing Anzhen Hospital. During hospitalization, the Omicron variant mortality rate was found to be 24.4%. Using the datasets of clinical demographics and laboratory tests, three machine learning algorithms, including best subset selection, stepwise selection, and least absolute shrinkage and selection operator regression analyses were employed to identify the potential predictors of in-hospital mortality. The results found that a panel of twenty-four clinical variables (including age, hyperlipemia, stroke, tumor, and several cardiovascular markers) identified by stepwise selection model exhibited significant performances in predicting the in-hospital mortality of COVID-19. The resultant nomogram showed good discrimination, highlighted by the areas under the curve values of 0.88 for 10 days, 0.81 for 20 days, and 0.82 for 30 days, respectively. Furthermore, decision curve analysis showed a significant reliability and precision for the established stepwise selection model. Collectively, this study developed an accurate and convenience risk model for predicting the in-hospital mortality of COVID-19 Omicron.

Morbidity and mortality of patients with peritoneal malignancy following cytoreductive surgery and hyperthermic intraperitoneal chemotherapy.

BACKGROUND: The purpose of this study was to record the incidence, and identify the prognostic variables of morbidity and mortality in patients with peritoneal malignancy undergoing cytoreductive surgery (CRS) combined with hyperthermic intraperitoneal chemotherapy (HIPEC). METHODS: The files of patients with peritoneal malignancy who underwent CRS + HIPEC from 2015-2022 were retrieved. Morbidity and hospital mortality were recorded and correlated to a variety of clinical variables. RESULTS: A total of 44/192 (22.9%) patients were recorded with postoperative complications. Grade 3 and 4 complications were 12.5%. The possible prognostic variables of morbidity were the extent of peritoneal malignancy and the number of suture lines. The mortality rate was 2.5% (5 patients). The number of FFP units, and peritonectomy procedures were identified as possible prognostic variables of hospital mortality. CONCLUSIONS: The morbidity rate in patients undergoing CRS + HIPEC is acceptable compared to morbidity of previous publications or major gastrointestinal surgical operations. The possible prognostic variables of morbidity are the extent of peritoneal malignancy, and the number of suture lines. The mortality rate is low. The possible prognostic variables of mortality are the number of FFP units, and the number of peritonectomy procedures.