COVID-19 In-hospital Mortality Research Articles

Prediction of COVID-19 in-hospital mortality in older patients using artificial intelligence: a multicenter study

Prediction of COVID-19 in-hospital mortality in older patients using artificial intelligence: a multicenter study

Covid-19 in-hospital fatality among hospitalized patient residents of the two largest and smaller cities of the Brazilian states

Objective: To estimate the relative risk of COVID-19 in-hospital mortality rates among patients according to residing in the two largest cities of each state or residing in any smaller city, and to describe different patterns across states and macro-regions of Brazil. Method: The sample included all hospitalizations confirmed for COVID-19 that occurred in Brazil with a recorded outcome of the hospitalization by the end of December 2021. The exposure assessed was patients residing in the two largest cities of the state or any smaller cities. The outcome was in-hospital mortality rates caused by COVID-19. Multilevel Poisson regression was used to estimate adjusted relative risks for each state and macro-region of Brazil. Result: The analytic sample was composed of 1,658,934 hospitalized COVID-19 cases. For the whole country, living in the two largest cities of each state was associated with a lower risk of in-hospital mortality rate. Nevertheless, in two states of the North region (Amazonas and Pará), individuals living in the largest cities presented greater in-hospital fatality rates than those living in smaller cities. Conclusion: At the two biggest states of the Brazilian North region, the pattern of association between living in the largest cities and in-hospital fatality was inverse, and higher proportions of deaths without access to a hospital bed were identified, indicating singular vulnerability. The findings highlight the need for a more equitable distribution of health care services and qualified professionals in the vast territory of Brazil.

Association between renin-angiotensin antagonism and COVID-19-related mortality in patients with essential hypertension: A single center, retrospective cohort study.

There is conflicting evidence in select mouse models and humans that suggest angiotensin-converting enzyme 2 expression is increased due to treatment with angiotensin converting enzyme inhibitors and angiotensin receptor blockers (ACEI/ARBs). Given the wide range of conditions that these medications treat, further evaluation is necessary to determine safety in the context of COVID-19. We sought to determine the association between use of ACEI/ARBs and COVID-19 severity in patients with essential hypertension. We included 714 patients with essential hypertension diagnosed with COVID-19 and admitted to University of Iowa Healthcare from March 1, 2020 to June 29, 2021. Severity of COVID-19 infection was assessed based on mortality, length of stay in hospital, intensive care unit admission, and use of supplemental oxygen, invasive ventilation, and vasopressors. Multivariable logistic and linear regression analyses were used for binary and continuous outcomes, respectively. Prior exposure to ACEI/ARBs before admission was significantly associated with lower mortality (OR: 0.454, p=.015), shorter length of stay in hospital (p<.001), and decreased adjusted odds of intensive care admission (OR: 0.719; p<.042). The present results suggest that patients with essential hypertension hospitalized with COVID-19 who had a prescription for ACEI/ARBs prior to admission exhibited less severe COVID-19 and lower in-hospital mortality.

Development and validation of a symbolic regression-based machine learning method to predict COVID-19 in-hospital mortality among vaccinated patients

Development and validation of a symbolic regression-based machine learning method to predict COVID-19 in-hospital mortality among vaccinated patients

Development of a long noncoding RNA-based machine learning model to predict COVID-19 in-hospital mortality

Tools for predicting COVID-19 outcomes enable personalized healthcare, potentially easing the disease burden. This collaborative study by 15 institutions across Europe aimed to develop a machine learning model for predicting the risk of in-hospital mortality post-SARS-CoV-2 infection. Blood samples and clinical data from 1286 COVID-19 patients collected from 2020 to 2023 across four cohorts in Europe and Canada were analyzed, with 2906 long non-coding RNAs profiled using targeted sequencing. From a discovery cohort combining three European cohorts and 804 patients, age and the long non-coding RNA LEF1-AS1 were identified as predictive features, yielding an AUC of 0.83 (95% CI 0.82–0.84) and a balanced accuracy of 0.78 (95% CI 0.77–0.79) with a feedforward neural network classifier. Validation in an independent Canadian cohort of 482 patients showed consistent performance. Cox regression analysis indicated that higher levels of LEF1-AS1 correlated with reduced mortality risk (age-adjusted hazard ratio 0.54, 95% CI 0.40–0.74). Quantitative PCR validated LEF1-AS1’s adaptability to be measured in hospital settings. Here, we demonstrate a promising predictive model for enhancing COVID-19 patient management.

Impact of liver fibrosis on COVID-19 in-hospital mortality in Southern Italy.

SARS-Cov-2 infection manifests as a wide spectrum of clinical presentation and even now, despite the global spread of the vaccine, contagiousness is still elevated. The aim of the study was the evaluation of the impact of liver fibrosis assessed by FIB-4 and liver impairment, assessed by cytolysis indices, on intrahospital mortality in COVID-19 subjects. This is a retrospective observational cohort study, which involved 23 COVID Hospital Units in Campania Region, Italy. Exposure variables were collected during hospital admission and at discharge. According to FIB-4 values, we subdivided the overall population in three groups (FIB-4<1.45; 1.45<FIB-4<3.25; FIB-4>3.25), respectively group 1,2,3. At the end of the study, 938 individuals had complete discharged/dead data. At admission, 428 patients were in group 1 (45.6%), 387 in group 2 (41.3%) and 123 in group 3 (13.1%). Among them, 758 (81%) subjects were discharged, while the remaining 180 (19%) individuals died. Multivariable Cox's regression model showed a significant association between mortality risk and severity of FIB-4 stages (group 3 vs group 1, HR 2.12, 95%CI 1.38-3.28, p<0.001). Moreover, Kaplan-Meier analysis described a progressive and statistically significant difference (p<0.001 Log-rank test) in mortality according to FIB-4 groups. Among discharged subjects, 507 showed a FIB-4<1.45 (66.9%, group 1), 182 a value 1.45<FIB-4<3.25 (24.1%, group 2) and 69 a FIB-4>3.25 (9.0%, group 3). Among dead subjects, 42 showed a FIB-4<1.45 (23.3%, group 1), 62 a value 1.45<FIB-4<3.25 (34.4%, group 2) and 76 a FIB-4>3.25 (42.3%, group 3). FIB-4 value is significantly associated with intrahospital mortality of COVID-19 patients. During hospitalization, particularly in patients with worse outcomes, COVID-19 seems to increase the risk of acute progression of liver damage.

Is Steatotic Liver Disease Related to Poor Outcome in COVID-19-Hospitalized Patients?

Background: Steatotic liver disease (SLD) has been linked to more exacerbated inflammatory responses in various scenarios. The relationship between SLD and COVID-19 prognosis remains unclear. Our aim was to investigate the impact of SLD on the outcome of COVID-19. Methods: Patients hospitalized with confirmed COVID-19 and who underwent laboratory tests and chest CT scans were included. SLD was assessed by measuring the attenuation coefficient on CT scans. The relationship between SLD, the severity of COVID-19 clinical presentation and in-hospital mortality were assessed. Results: A total of 610 patients were included (mean age 62 ± 16 years, 64% male). The prevalence of SLD was 30%, and the overall in-hospital mortality rate was 19%. Patients with SLD were younger (58 ± 13 vs. 64 ± 16 years, p < 0.001) and had a higher BMI (32 ± 5 vs. 28 ± 4 kg/m2, p = 0.014). Admission AST values were higher in patients with SLD (82 ± 339 vs. 50 ± 37, p = 0.02), while D-dimer (1112 ± 2147 vs. 1959 ± 8509, p = 0.07), C-reactive protein (12 ± 9 vs. 11 ± 8, p = 0.27), ALT (67 ± 163 vs. 47 ± 90, p = 0.11), ALP (83 ± 52 vs. 102 ± 125, p = 0.27), and GGT (123 ± 125 vs. 104 ± 146, p = 0.61) did not significantly differ compared to patients without SLD. No difference was observed regarding lung parenchyma involvement >50% (20% vs. 17%, p = 0.25), hospital length of stay (14 ± 19 vs. 16 ± 23 days, p = 0.20), hemodialysis support (14% vs. 16%, p = 0.57), use of mechanical ventilation (20% vs. 20%, p = 0.96), and in-hospital mortality (17% vs. 20%, p = 0.40) when comparing patients with and without SLD. Conclusions: SLD showed no significant association with morbidity and mortality in patients with COVID-19.

Identification of predictive patient characteristics for assessing the probability of COVID-19 in-hospital mortality.

As the world emerges from the COVID-19 pandemic, there is an urgent need to understand patient factors that may be used to predict the occurrence of severe cases and patient mortality. Approximately 20% of SARS-CoV-2 infections lead to acute respiratory distress syndrome caused by the harmful actions of inflammatory mediators. Patients with severe COVID-19 are often afflicted with neurologic symptoms, and individuals with pre-existing neurodegenerative disease have an increased risk of severe COVID-19. Although collectively, these observations point to a bidirectional relationship between severe COVID-19 and neurologic disorders, little is known about the underlying mechanisms. Here, we analyzed the electronic health records of 471 patients with severe COVID-19 to identify clinical characteristics most predictive of mortality. Feature discovery was conducted by training a regularized logistic regression classifier that serves as a machine-learning model with an embedded feature selection capability. SHAP analysis using the trained classifier revealed that a small ensemble of readily observable clinical features, including characteristics associated with cognitive impairment, could predict in-hospital mortality with an accuracy greater than 0.85 (expressed as the area under the ROC curve of the classifier). These findings have important implications for the prioritization of clinical measures used to identify patients with COVID-19 (and, potentially, other forms of acute respiratory distress syndrome) having an elevated risk of death.

Association between COVID-19 Diagnosis and In-Hospital Mortality of Hospitalized patients with ST-Segment Elevation Myocardial Infarction

Association between COVID-19 Diagnosis and In-Hospital Mortality of Hospitalized patients with ST-Segment Elevation Myocardial Infarction

Regional variation of COVID-19 admissions, acute kidney injury and mortality in England - a national observational study using administrative data

BackgroundThis study explores regional variations in COVID-19 hospitalization rates, in-hospital mortality, and acute kidney injury (AKI) in England. We investigated the influence of population demographic characteristics, viral strain changes, and therapeutic advances on clinical outcomes.MethodsUsing hospital episode statistics, we conducted a retrospective cohort study with 749,844 admissions in 337,029 adult patients with laboratory-confirmed COVID-19 infection (March 1, 2020, to March 31, 2021). Multivariable logistic regression identified factors predicting AKI and mortality in COVID-19 hospitalized patients.ResultsLondon had the highest number of COVID-19 admissions (131,338, 18%), followed by the North-west region (122,683, 16%). The North-west had the highest population incidence of COVID-19 hospital admissions (21,167 per million population, pmp), while the South-west had the lowest (9,292 admissions pmp). Patients in London were relatively younger (67.0 ± 17.7 years) than those in the East of England (72.2 ± 16.8 years). The shortest length of stay was in the North-east (12.2 ± 14.9 days), while the longest was in the North-west (15.2 ± 17.9 days). All eight regions had higher odds of death compared to London, ranging from OR 1.04 (95% CI 1.00, 1.07) in the South-west to OR 1.24 (95% CI 1.21, 1.28) in the North-west. Older age, Asian ethnicity, emergency admission, transfers from other hospitals, AKI presence, ITU admission, social deprivation, and comorbidity were associated with higher odds of death. AKI incidence was 30.3%, and all regions had lower odds of developing AKI compared to London. Increasing age, mixed and black ethnicity, emergency admission, transfers from other providers, ITU care, and different levels of comorbidity were associated with higher odds of developing AKI.ConclusionsLondon exhibited higher hospital admission numbers and AKI incidence, but lower odds of death compared to other regions in England.Trial registrationRegistered on National Library of Medicine website (www.clinicaltrials.gov) with registration number NCT04579562 on 8/10/2020.

Pre-COVID-19 hospital quality and hospital response to COVID-19: examining associations between risk-adjusted mortality for patients hospitalised with COVID-19 and pre-COVID-19 hospital quality

ObjectivesThe extent to which care quality influenced outcomes for patients hospitalised with COVID-19 is unknown. Our objective was to determine if prepandemic hospital quality is associated with mortality among Medicare...

Type 2 Diabetes Status, Diabetes Complication Severity Index Scores, and Their Relationship With COVID-19 Severity: A Retrospective Cohort Study of Hospitalized Patients in a Southwest Virginia Health System.

Background Studies have shown that patients with type 2 diabetes mellitus (T2DM) tend to have poorer outcomes associated with COVID-19, including increased rates of hospitalization, ICU admission, need for ventilatory support, and mortality. Methods We performed a retrospective cohort study that included all non-pregnant adult patients who were hospitalized as a result of COVID-19 in a Southwest Virginia health system between March 18, 2020, and August 31, 2022. T2DM status was treated as a binary variable. T2DM severity was assessed using the Diabetes Complications Severity Index (DCSI). Multivariate logistic regression was used to assess the relationship between T2DM status and COVID-19 severity outcomes. Multivariate logistic regression was also used to assess the relationship between DCSI score and COVID-19 severity outcomes among patients with an established diagnosis of T2DM at the time of COVID-19 hospital admission. Results Patients with T2DM had 1.27 times the odds of experiencing a poor COVID-19 clinical outcome (95% CI: 1.13, 1.43) and 1.35 times the odds of in-hospital mortality (95% CI: 1.14, 1.59) compared to patients without diabetes. Among patients with T2DM, increasing DCSI score was significantly associated with increased odds of experiencing a poor COVID-19 clinical outcome and in-hospital mortality. Conclusions Diabetic patients in our sample were at increased odds of experiencing poor COVID-19 clinical outcomes and in-hospital mortality compared to individuals without diabetes. Amongst patients with T2DM, increasing DCSI score was associated with worse COVID-19 outcomes. Clinical decision support tools may be able to utilize DCSI scores as an indicator of COVID-19 severity risk to facilitate decisions regarding treatment aggressiveness and resource allocation.

Atorvastatin Effect on COVID-19 Outcomes: A Propensity Score Matched Study on Hospitalized Patients.

This study investigated the association of atorvastatin use on survival, need for intensive care unit (ICU) admission, and length of hospital stay (LOS) among COVID-19 inpatients. A retrospective study was conducted between March 20th, 2020, and March 18th, 2021, on patients with confirmed COVID-19 admitted to three hospitals in Tehran, Iran. The unadjusted and adjusted effects of atorvastatin on COVID-19 prognosis were investigated. Propensity score matching (PSM) was used to achieve a 1:1 balanced dataset with a caliper distance less than 0.1 and the nearest neighbor method without replacement. Of 4322 COVID-19 patients, 2136 (49.42%) were treated with atorvastatin. After PSM, 1245 atorvastatin inpatients and 1245 controls were included with a median age of 62.0 (interquartile range [IQR]: 51.0, 76.0) and 63.0 (IQR: 51.0, 75.0) years, respectively. The standardized mean differences were less than 0.1 for all confounders, suggesting a good covariate balance. The use of atorvastatin was associated with decreased COVID-19 mortality (HR: 0.80; 95% CI: 0.68-0.95), whereas no relationship was found between atorvastatin and the need for ICU admission (HR: 1.21; 95% CI: 0.99-1.47). LOS was significantly higher in the atorvastatin cohort than controls (Atorvastatin vs. others: 7 [5, 11] vs. 6 [4, 10] days; p = 0.003). The survival rate was higher in combination therapy of atorvastatin plus enoxaparin than in those who received atorvastatin alone (p-value=0.001). Atorvastatin may reduce the risk of COVID-19 in-hospital mortality and could be a beneficial option for an add-on therapy. Randomized trials are warranted to confirm the results of the current observational studies.

Risk factors for COVID-19 in-hospital mortality in Argentina: A competing risk survival analysis.

COVID-19 poses dire threats for low and middle-income countries (LMICs). Yet, there remains limited rigorous evidence describing the characteristics and outcomes of hospitalized patients for LMICs, and often the evidence was based on small samples and/or unicentric. The objective of this study was to examine risk factors of COVID-19 mortality in Argentina, a hard-hit middle-income Latin American country. We analyze data on 5,146 COVID-19 patients from 11 centers across 10 cities in Argentina, making this one of the largest multi-centric retrospective observational descriptive studies in the LMICs. Information on demographics and co-morbidities was extracted from medical records. Outcomes of relevance consisted of whether the patient was discharged or deceased (as established in medical records), along with date of each event. We use survival models that account for competing risks. Median age was 60 years (IQR: 48-72), there were fewer women (40.8%) hospitalized than men (59.2%), and the most prevalent comorbidities were hypertension (40.9%), diabetes (20.0%) and obesity (19.1%). Patients were hospitalized for a median duration of 8 days (IQR: 5-13), and in-hospital mortality was 18.1%, though it varied substantially across health centers (95%CI: 17.1%-19.2%). Baseline characteristics most associated with in-hospital mortality were respiratory rate (adjusted HR = 3.6, 95%CI: 2.5-5.4 for ≥ 26 breathes/min), older age (adjusted HR = 2.5, 95%CI: 2.0-3.3 for the 80+ age group), and chronic kidney disease (adjusted HR = 2.2, 95%CI: 1.8-2.8). Associations were attenuated when survival models did not account for the competing risk of being discharged. We document lower mortality rates than those in prior studies, likely due to a lower prevalence of comorbidities amongst patients in our sample. Compared with standard Cox models, we find that, when using competing risk models, risk factors have a larger role in explaining COVID-19 mortality. Overall, we provide rigorous evidence describing the characteristics and outcomes of hospitalized patients for LMICs. Thus, our findings are useful to conduct a more accurate in-hospital monitoring of patient subgroups who may be at greater risk. They also provide valuable guidance for public health and policy efforts in Argentina and other developing countries.

Impact of Acute Kidney Injury on the COVID-19 In-Hospital Mortality in Octogenarian Patients: Insights from the COVOCA Study.

The COVID-19 pandemic, caused by the novel coronavirus SARS-CoV-2, has fundamentally reshaped the landscape of global public health, with some people suffering more adverse clinical outcomes than others. The aim of this study is to deepen our understanding of the specific impact of acute kidney injury (AKI) on the in-hospital mortality in octogenarian patients with COVID-19. This is a prospective observational cohort study, which involved 23 COVID-19 hospital units in the Campania Region, Italy. Exposure variables were collected during hospital admission and at discharge. Only patients aged ≥80 years were deemed eligible for the study. 197 patients were included in the study (median age 83.0 [82.0-87.0] years; 51.5% men), with a median duration of hospitalization of 15.0 [8.0-25.0] days. From the multivariable Cox regression analysis, after the application of Šidák correction, only the respiratory rate (HR 1.09, 95% CI: 1.04 to 1.14; p < 0.001) and AKI development (HR: 3.40, 95% CI: 1.80 to 6.40; p < 0.001) were independently associated with the primary outcome. Moreover, the Kaplan-Meier analysis showed a significantly different risk of in-hospital mortality between patients with and without AKI (log-rank: <0.0001). In our investigation, we identified a significant association between AKI and mortality rates among octogenarian patients admitted for COVID-19. These findings raise notable concerns and emphasize the imperative for vigilant monitoring of this demographic cohort.

Latent Tuberculosis Infection and COVID-19: Analysis of a Cohort of Patients from Careggi University Hospital (Florence, Italy).

Data regarding the relationship between coronavirus disease (COVID-19) and active or latent tuberculosis (TB) are discordant. We conducted a retrospective study examining the impact of latent tuberculosis infection (LTBI) on the clinical progression of COVID-19 patients. We selected 213 patients admitted with COVID-19 in a tertiary-level Italian hospital (February-December 2020), who underwent a QuantiFERON-TB test (QFT) and/or chest radiological exam. The population was divided into three groups: (i) QFT negative and without radiological TB sequelae (Neg); (ii) QFT positive and without radiological TB sequelae (Pos); (iii) radiological TB sequelae regardless of QFT result (Seq). In-hospital mortality and oro-tracheal intubation (OTI) showed significantly higher results in the Seq group (Seq 50% vs. Pos 13.3% vs. Neg 9.3%, p < 0.001; Seq 16.7% vs. Pos 6.7% vs. Neg 4.9%, p = 0.045). Considering the Pos and Seq groups' patients as the population with defined LTBI, in-hospital mortality (20/51, 39.2%) and OTI risk (7/51, 13.7%) were statistically higher with respect to patients without LTBI (in-hospital mortality: 15/162, 9.3%, p < 0.001; OTI risk: 8/162, 4.9%, p = 0.023), respectively. Multivariate analysis showed that radiological sequelae and the Charlson Comorbidity Index (CCI) were significantly associated with higher mortality rate; despite the higher CCI of Seq population, we cannot exclude the correlation between COVID-19 in-hospital mortality and the presence of radiological TB sequelae.

CD147 Plasma Levels in Hospitalised Patients with Covid-19 Pneumonia Predict Illness Severity and In-Hospital Mortality.

COVID-19 and infectious diseases have been included in strategic development goals (SDG) of United Nations (UN). The CD147 receptor is one of several receptors for the SARS-CoV-2 spike protein that could mediate Covid-19 viral infection of host cells. It has been recently proposed to regulate viral invasion and dissemination among lymphocytes and progenitor/stem cells. A soluble by-product of CD147 (sCD147) exists in plasma and has been previously identified as a marker of diabetes and platelet activation. We examined plasma sCD147 levels in 161 Covid-19 patients at hospital admission. We demonstrated significantly higher plasma sCD147 levels in Covid-19 patients, which correlated with plasma multiorgan dysfunction biomarkers interleukin-6, creatinine and Troponin I. Importantly, sCD147 admission levels were associated with Covid-19 severity and survival, carrying potential value as a biomarker in hospitalized patients with Covid-19 infection.

The Effect of ESKD on COVID-19 In-Hospital Mortality

The Effect of ESKD on COVID-19 In-Hospital Mortality

Increased risk of poor clinical outcome in COVID-19 patients with diabetes mellitus and in-hospital mortality predictors: Aretrospective cohort from a tertiary hospital in Indonesia.

To determine association between diabetes in confirmed cases of COVID-19 and intensive care admission and in-hospital mortality, evaluate several laboratory parameters as mortality predictor and develop predictors of in-hospital mortality among diabetics with COVID-19. This retrospective cohort recruited all cases of COVID-19 hospitalized in Fatmawati General Hospital from March to October 2020. Inclusion criterion was RT-PCR confirmed cases of COVID-19 who aged 18 years and older while exclusion criteria were incomplete medical record or cannot be found and pregnant women. We enrolled 506 participants to this study with median age of 51 years (IQR:22), female (56.32%), and diabetes (28.46%). Diabetes increased intensive care admission (adjusted OR: 2.57; 95% CI: 3.52-10.43) and in-hospital mortality (adjusted OR: 2.50; 95% CI: 1.61-3.89). In predicting in-hospital mortality, ferritin and lactate dehydrogenase offered an acceptable discrimination, AUC: 0.71 (95% CI: 0.62-0.79) and AUC: 0.70 (95% CI: 0.61-0.78), respectively. The optimal cut-off of predicting mortality for ferritin was 786 g/mL and for LDH was 514.94 u/L. Factors include age above 70 years old, RBGs level on admission above 250 mg/dL or below 140 mg/dL, ferritin level above 786 ng/mL and presence of ARDS increased the odds of mortality among individuals with diabetes. Diabetes increases risk intensive care admission and in hospital mortality in COVID-19. Multivariate analysis showed that older age, RBG on admission, high ferritin level, presence of ARDS increased the odds of mortality among individuals with diabetes.

OC 29.3 CD147 Plasma Levels in Hospitalised Patients with Covid-19 Pneumonia Predict Illness Severity and In-Hospital Mortality

OC 29.3 CD147 Plasma Levels in Hospitalised Patients with Covid-19 Pneumonia Predict Illness Severity and In-Hospital Mortality