In-hospital Mortality In COVID-19 Patients Research Articles

Low Oxygen Saturation of COVID-19 in Patient Case Fatalities, Limpopo Province, South Africa

On 1 August 2020, South Africa’s Minister of Health announced that more than half of a million coronavirus cases were confirmed in the country. South Africa was by far the hardest-hit country in Africa, accounting for half of all infections reported across the continent. The prevalence of underlying conditions such as fever and blood oxygen saturation (SpO2) has been known known to be a significant determinant in the hospitalisation of COVID-19 patients. Low oxygen saturation on admission was reported as a strong predictor of in-hospital mortality in COVID-19 patients. The study sought to assess the association between body temperature and other clinical risk factors with low SpO2 among COVID-19 inpatient case fatalities. A quantitative retrospective study was carried out in Limpopo Province, employing secondary data from the Limpopo Department of Health (LDoH) on COVID-19 inpatients case fatalities across all districts in the province. The chi-square test and Pearson’s correlation coefficient were used to assess the relationship between body temperature and clinical risk factors with SpO2 levels. The findings of this paper indicated that age (older age), chills, sore throat, anosmia, dysgeusia, myalgia/body aches, diarrhoea and HIV/AIDS were associated with low SpO2 in-hospital mortality in COVID-19 patients. Nasal prongs and a face mask with a reservoir for respiratory support cannula were commonly used patient interfaces to provide supplemental oxygen, with the use of only a high-flow nasal cannula (HFNC) being minimal (7%). The majority of COVID-19 inpatient fatalities had normal body temperature (<38 °C) and SpO2, with no correlation between the two variables. Considering temperature screening as a possible strategy to combat the spread of COVID-19 or suspicious COVID-19 cases appeared, then, to be a pointless exercise. This study aimed to recommend new clinical criteria for detecting COVID-19 cases.

Age-Dependent Biomarkers for Prediction of In-Hospital Mortality in COVID-19 Patients.

Background: Several biomarkers and models have been proposed to predict in-hospital mortality among COVID-19 patients. However, these studies have not examined the association in sub-populations. The present study aimed to identify the association between the two most common inflammatory biomarkers in the emergency department and in-hospital mortality in subgroups of patients. Methods: A historical cohort study of adult patients who were admitted to acute-care hospital between March and December 2020 and had a diagnosis of COVID-19 infection. Data on age, sex, Charlson comorbidity index, white blood cell (WBC) count, C-reactive protein (CRP), and in-hospital mortality were collected. Discrimination ability of each biomarker was observed and the CHAID method was used to identify the association in subgroups of patients. Results: Overall, 762 patients (median age 70.9 years, 59.7% males) were included in the study. Of them, 25.1% died during hospitalization. In-hospital mortality was associated with higher CRP (median 138 mg/L vs. 85 mg/L, p < 0.001), higher WBC count (median 8.5 vs. 6.6 K/µL, p < 0.001), and higher neutrophil-to-lymphocyte ratio (NLR) (median 9.2 vs. 5.4, p < 0.001). The area under the ROC curve was similar among all biomarkers (WBC 0.643, NLR 0.677, CRP 0.646, p > 0.1 for all comparisons). The CHAID method revealed that WBC count was associated with in-hospital mortality in patients aged 43.1–66.0 years (<11 K/µL: 10.1% vs. 11+ K/µL: 27.9%), NLR in patients aged 66.1–80 years (≤8: 15.7%, >8: 43.3%), and CRP in patients aged 80.1+ years (≤47 mg/L: 18.8%, 47.1–149 mg/L: 43.1%, and 149.1+: 71.7% mortality). Conclusions: WBC, NLR, and CRP present similar discrimination abilities. However, each biomarker should be considered as a predictor for in-hospital mortality in different age groups.

Cardiac biomarkers on admission and in‑hospital mortality in COVID-19 patients with or without concomitant heart failure.

High‑sensitivity cardiac troponin T (hs-cTnT) and N-terminal pro-B type natriuretic peptide (NT‑ proBNP) are known markers of cardiac injury. However, their role in predicting the severity of COVID‑19 remains to be investigated. We aimed to analyze the association between hs‑cTnT and NT-proBNP levels and in hospital mortality in patients with COVID‑19, with emphasis on those with concomitant chronic heart failure (CHF). A total of 1729 consecutive patients with COVID‑19 were enrolled. Demographic data, laboratory parameters, and clinical outcomes (discharge or death) were analyzed. Receiver operating characteristic (ROC) and logistic regression analyses were performed to evaluate the association between hs‑cTnT and NT-proBNP values and the risk of death. Evaluation of hs‑cTnT was performed in 1041 patients, while NT-proBNP was assessed in 715 individuals. CHF was present in 179 cases (10.4% of the cohort). Median values of hs‑cTnT and NT-proBNP and in‑hospital mortality were higher in CHF patients than in those without CHF. Among patients without CHF, mortality was the highest in those with hs‑cTnT or NT-proBNP values in the fourth quartile. In ROC analysis, hs‑cTnT equal to or above 142 ng/l and NT-proBNP equal to or above 969 pg/ml predicted in‑hospital death. In patients without CHF, each 10-ng/l increase in hs-cTnT or 100-pg/ml increase in NT‑proBNP was associated with a higher risk of death (odds ratio [OR], 1.01 and OR, 1.02, respectively; P <0.01 for both). The level of hs‑cTnT or NT-proBNP predicts in hospital mortality in COVID-19 patients. Both hs‑cTnT and NT-proBNP should be routinely measured on admission in all patients hospitalized due to COVID‑19 for early detection of individuals with an increased risk of in hospital death, even if they do not have concomitant heart failure.

Abstract 111: Multiparametric Circulating Cardiovascular Biomarkers Elucidate A Molecular Signature Of Coronavirus Disease 2019 Mortality And Identify Vascular Barrier Stabilizing Therapies

Background: Endothelial cell (EC) activation, endotheliitis, vascular permeability, and thrombosis have been observed in patients with severe COVID-19, indicating that the vasculature is affected during the acute stages of SARS-CoV-2 infection. It remains unknown whether circulating vascular markers are sufficient to predict clinical outcomes, unique to COVID-19 related pathology, and if vascular permeability can be therapeutically targeted. Methods: This is a secondary analysis of a prospectively recruited longitudinal multicenter cohort study enrolling 241 patients with suspected SARS-CoV-2 infection. The prevalence of circulating inflammatory, cardiac, EC activation, and the entirety of the microRNA transcriptome was evaluated, and the prognostic value assessed using a Random Forest model machine learning approach. Ex vivo experiments were performed to assess EC permeability responses to patient plasma and to derive modulated gene regulatory networks from which rational therapeutic selection could be inferred. Results: Multiple inflammatory and EC activation biomarkers were associated with mortality in COVID-19 patients and in severity-matched SARS-CoV-2-negative patients. In contrast, dysregulation of particular microRNAs at presentation was specific for poor COVID-19-related outcomes and revealed disease-relevant pathways. Integrating datasets (i.e., clinical, protein, and microRNA) using a machine learning approach further enhanced clinical risk prediction for in-hospital mortality. Exposure of ECs to COVID-19 patient plasma resulted in severity-specific gene expression responses and EC barrier dysfunction. ECs treated with patient plasma showed increased vascular permeability, which was ameliorated by treatment with the vascular stabilizing molecules angiopoietin-1 mimetic or recombinant Slit2-N, but not nangibotide or dexamethasone. Conclusions: Integration of multi-omics data identified microRNA and vascular biomarkers prognostic of in-hospital mortality in COVID-19 patients and revealed that vascular stabilizing therapies should be explored as a treatment for endothelial dysfunction in COVID-19, and other severe diseases where endothelial dysfunction has a central role in pathogenesis.

Simplified Acute Physiology Score 3 Performance in Austrian COVID-19 Patients Admitted to Intensive Care Units with and without Diabetes.

This study evaluated and compared the performance of simplified acute physiology score 3 (SAPS 3) for predicting in-hospital mortality in COVID-19 patients admitted to intensive care units (ICUs) with and without diabetes in Austria. The Austrian national public health institute (GÖG) data of COVID-19 patients admitted to ICUs (n = 5850) were analyzed. Three versions of SAPS 3 were used: standard equation, Central European equation, and Austrian equation customized for COVID-19 patients. The observed in-hospital mortality was 38.9%, 42.9%, and 37.3% in all, diabetes, and non-diabetes patients, respectively. The overall C-statistics was 0.69 with an insignificant (p = 0.193) difference between diabetes (0.70) and non-diabetes (0.68) patients. The Brier score was > 0.20 for all SAPS 3 equations in all cohorts. Calibration was unsatisfactory for both standard and Central European equations in all cohorts, whereas it was satisfactory for the Austrian equation in diabetes patients only. The SAPS 3 score demonstrated low discrimination and accuracy in Austrian COVID-19 patients, with an insignificant difference between diabetes and non-diabetes. All equations were miscalibrated particularly in non-diabetes patients, while the Austrian equation showed satisfactory calibration in diabetes patients only. Both uncalibrated and calibrated versions of SAPS 3 should be used with caution in COVID-19 patients.

Systemic Inflammatory Predictors of In-Hospital Mortality in COVID-19 Patients: A Retrospective Study.

The purpose of this study was to investigate whether routine blood tests and clinical characteristics can predict in-hospital mortality in COVID-19. Clinical data of 285 patients aged 59.7 ± 10.3 yrs. (males n = 189, females n = 96) were retrospectively collected from December 2020 to June 2021. Routine blood tests were recorded within the 1st hour of admission to hospital. The inflammatory variables, such as C-reactive protein (CRP), procalcitonin (PCT), neutrophils–lymphocyte ratio (NLR) and the systemic inflammatory index (SII), exceeded the reference values in all patients and were significantly higher in deceased patients (n = 108) compared to survivors (n = 177). The log-rank test for comparing two survival curves showed that patients aged ≥60.5 years, with PCT ≥ 0.188 ng/mL or NLR ≥ 11.57 103/µL were at a greater risk of death. NLR demonstrated a high impact on the COVID-19 mortality (HR 1.317; 95%CI 1.004–1.728; p < 0.05), whereas CRP and SII showed no effect (HR 1.000; 95%CI 1.000–1.004; p = 0.085 and HR 1.078; 95%CI 0.865–1.344; p = 0.503, respectively). In the first Polish study including COVID-19 patients, we demonstrated that age in relation to simple parameters derived from complete blood cell count has prognostic implications in the course of COVID-19 and can identify the patients at a higher risk of in-hospital mortality.

Factors predicting in-hospital all-cause mortality in COVID 19 patients at the Laquintinie Hospital Douala, Cameroon

BackgroundDespite being a global pandemic, little is known about the factors influencing in-hospital mortality of COVID-19 patients in sub-Saharan Africa. This study aimed to provide data on in-hospital mortality among COVID-19 patients hospitalized in a single large center in Cameroon. MethodsA hospital-based prospective follow-up was conducted from March 18 to June 30, 2020, including patients >18 years with positive PCR for SARS-COV-2 on nasopharyngeal swab admitted to the Laquintinie Douala hospital COVID unit. Predictors of in-hospital mortality were assessed using Kaplan Meir survival curves and Weibull regression for the accelerated time failure model. Statistical significance was considered as p < 0.05. ResultsOverall 712 patients (65,7% men) were included, mean age 52,80 ± 14,09 years. There were 580 (67,8% men) in-hospital patients. The median duration of hospital stay was eight days. The in-hospital mortality was 22.2%. Deceased patients compared to survivors were significantly older, had a higher temperature, respiratory rate, and heart rate, and lowest peripheral oxygen saturation at admission. After adjusting for age, sex, and other clinical patient characteristics, increased heart rate, increased temperature, decreased peripheral oxygen saturation. The critical clinical status was significantly associated with increased in-hospital mortality. In contrast, hospitalization duration greater than eight days and the use of hydroxychloroquine (HCQ) + azithromycin (AZM) therapy was associated with decreased risk of in-hospital mortality. ConclusionOne in five hospitalized COVID-19 patients die in a low-middle income setting. Critical clinical status, dyspnea, and increased heart rate were predictors of in-hospital mortality. This study will serve as a prerequisite for more robust subsequent follow-up studies. Also, these results will aid in revising national guidelines for the management of COVID-19 in Cameroon.

Evaluation and Comparison of the Predictive Value of 4C Mortality Score, NEWS, and CURB-65 in Poor Outcomes in COVID-19 Patients: A Retrospective Study from a Single Center in Romania.

To date, the COVID-19 pandemic has caused millions of deaths across the world. Prognostic scores can improve the clinical management of COVID-19 diagnosis and treatment. The objective of this study was to assess the predictive role of 4C Mortality, CURB-65, and NEWS in COVID-19 mortality among the Romanian population. A single-center, retrospective, observational study was conducted on patients with reverse transcriptase-polymerase chain reaction (RT-PCR)-proven COVID-19 admitted to the Municipal Emergency Clinical Hospital of Timisoara, Romania, between 1 October 2020 and 15 March 2021. Receiver operating characteristic (ROC) and area under the curve (AUC) analyses were performed to determine the discrimination accuracy of the three scores. The mean values of the risk scores were higher in the non-survivors group (survivors group vs. non-survivors group: 8 vs. 15 (4C Mortality Score); 3 vs. 8.5 (NEWS); 1 vs. 3 (CURB-65)). In terms of mortality risk prediction, the NEWS performed best, with an AUC of 0.86, and the CURB-65 score performed poorly, with an AUC of 0.80. CURB-65, NEWS, and 4C Mortality scores were significant mortality predictors in the analysis, with acceptable calibration. Among the scores assessed in our study, NEWS had the highest performance in predicting in-hospital mortality in COVID-19 patients. Thus, the findings from this study suggest that the use of NEWS may be beneficial to the early identification of high-risk COVID-19 patients and the provision of more aggressive care to reduce mortality associated with COVID-19.

Predictors of in-hospital mortality of COVID-19 patients and the role of telemetry in an internal medicine ward during the third phase of the pandemic.

The first pandemic phase of COVID-19 in Italy was characterized by high in-hospital mortality ranging from 23% to 38%. During the third pandemic phase there has been an improvement in the management and treatment of COVID-19, so mortality and predictors may have changed. A prospective study was planned to identify predictors of mortality during the third pandemic phase. From 15 December 2020 to 15 May 2021, 208 patients were hospitalized (median age: 64 years; males: 58.6%); 83% had a median of 2 (IQR,1-4) comorbidities; pneumonia was present in 89.8%. Patients were monitored remotely for respiratory function and ECG trace for 24 hours/day. Management and treatment were done following the timing and dosage recommended by international guidelines. 79.2% of patients necessitated O2-therapy. ARDS was present in 46.1% of patients and 45.4% received non-invasive ventilation and 11.1% required ICU treatment. 38% developed arrhythmias which were identified early by telemetry and promptly treated. The in-hospital mortality rate was 10%. At multivariate analysis independent predictors of mortality were: older age (R-R for≥70 years: 5.44), number of comorbidities ≥3 (R-R 2.72), eGFR ≤60 ml/min (RR 2.91), high d-Dimer (R-R for≥1,000 ng/ml:7.53), and low PaO2/FiO2 (R-R for <200: 3.21). Management and treatment adherence to recommendations, use of telemetry, and no overcrowding appear to reduce mortality. Advanced age, number of comorbidities, severe renal failure, high d-Dimer and low P/F remain predictors of poor outcome. The data help to identify current high-risk COVID-19 patients in whom management has yet to be optimized, who require the greatest therapeutic effort, and subjects in whom vaccination is mandatory.

D-dimer, CRP, PCT, and IL-6 Levels at Admission to ICU Can Predict In-Hospital Mortality in Patients with COVID-19 Pneumonia.

Introduction Health care workers have had a challenging task since the COVID-19 outbreak. Prompt and effective predictors of clinical outcomes are crucial to recognize potentially critically ill patients and improve the management of COVID-19 patients. The aim of this study was to identify potential predictors of clinical outcomes in critically ill COVID-19 patients. Methods The study was designed as a retrospective cohort study, which included 318 patients treated from June 2020 to January 2021 in the Intensive Care Unit (ICU) of the Clinical Hospital Center “Bezanijska Kosa” in Belgrade, Serbia. The verified diagnosis of COVID-19 disease, patients over 18 years of age, and the hospitalization in ICU were the criteria for inclusion in the study. The optimal cutoff value of D-dimer, CRP, IL-6, and PCT for predicting hospital mortality was determined using the ROC curve, while the Kaplan-Meier method and log-rank test were used to assess survival. Results The study included 318 patients: 219 (68.9%) were male and 99 (31.1%) female. The median age of patients was 69 (60-77) years. During the treatment, 195 (61.3%) patients died, thereof 130 male (66.7%) and 65 female (33.3%). 123 (38.7%) patients were discharged from hospital treatment. The cutoff value of IL-6 for in-hospital death prediction was 74.98 pg/mL (Sn 69.7%, Sp 62.7%); cutoff value of CRP was 81 mg/L (Sn 60.7%, Sp 60%); cutoff value of procalcitonin was 0.56 ng/mL (Sn 81.1%, Sp 76%); and cutoff value of D-dimer was 760 ng/mL FEU (Sn 63.4%, Sp 57.1%). IL-6 ≥ 74.98 pg/mL, CRP ≥ 81 mg/L, PCT ≥ 0.56 ng/mL, and D-dimer ≥ 760 ng/mL were statistically significant predictors of in-hospital mortality. Conclusion IL-6 ≥ 74.98 pg/mL, CRP values ≥ 81 mg/L, procalcitonin ≥ 0.56 ng/mL, and D-dimer ≥ 760 ng/mL could effectively predict in-hospital mortality in COVID-19 patients.

COVID-19 mortality prediction in the intensive care unit with deep learning based on longitudinal chest X-rays and clinical data.

ObjectivesWe aimed to develop deep learning models using longitudinal chest X-rays (CXRs) and clinical data to predict in-hospital mortality of COVID-19 patients in the intensive care unit (ICU).MethodsSix hundred fifty-four patients (212 deceased, 442 alive, 5645 total CXRs) were identified across two institutions. Imaging and clinical data from one institution were used to train five longitudinal transformer-based networks applying five-fold cross-validation. The models were tested on data from the other institution, and pairwise comparisons were used to determine the best-performing models.ResultsA higher proportion of deceased patients had elevated white blood cell count, decreased absolute lymphocyte count, elevated creatine concentration, and incidence of cardiovascular and chronic kidney disease. A model based on pre-ICU CXRs achieved an AUC of 0.632 and an accuracy of 0.593, and a model based on ICU CXRs achieved an AUC of 0.697 and an accuracy of 0.657. A model based on all longitudinal CXRs (both pre-ICU and ICU) achieved an AUC of 0.702 and an accuracy of 0.694. A model based on clinical data alone achieved an AUC of 0.653 and an accuracy of 0.657. The addition of longitudinal imaging to clinical data in a combined model significantly improved performance, reaching an AUC of 0.727 (p = 0.039) and an accuracy of 0.732.ConclusionsThe addition of longitudinal CXRs to clinical data significantly improves mortality prediction with deep learning for COVID-19 patients in the ICU.Key Points• Deep learning was used to predict mortality in COVID-19 ICU patients.• Serial radiographs and clinical data were used.• The models could inform clinical decision-making and resource allocation.Supplementary InformationThe online version contains supplementary material available at 10.1007/s00330-022-08588-8.

Hospitalization and in-hospital mortality due to COVID-19 in the Silesian Voivodeship during the first year of epidemic.

The Silesian Voivodeship is the region with the highest population density in Poland, three times higher than the national average. These are important circumstances that may favor the transmission of the SARS-CoV-2 virus and, due to the severe course of the disease in many patients, contribute to overloading the healthcare system. The aim of the study was to evaluate the temporal and territorial variability of hospitalized morbidity and in-hospital mortality in COVID-19 patients treated in the study region. The research was an epidemiological descriptive study. Secondary epidemiological data were obtained from the registry of the Health Department of the Silesian Voivodeship Office in Katowice. Crude and standardized hospitalized morbidity rates, as well as COVID-19 in-hospital mortality, were calculated. Results were presented in particular poviats and subsequent months of the 2020 year. The capabilities of the ArcGIS 9.2 geographic information system and Statistica 13.3 software were used. The largest number of patients hospitalized due to COVID-19 and the number of deaths occurred in autumn and spring, in both periods in-hospital mortality was over 24%. The poviats with the highest standardized hospitalized prevalence rate were located in the southern part of the Silesian Voivodeship. The highest in-hospital mortality (exceeding 24%) was recorded in Gliwice, Mysłowice, Tychy, Bytom and in the Lubliniec poviat. Older people (65+), usually with diagnosed comorbidities, including chronic respiratory and cardiovascular diseases, died more often. The highest number of patients and deaths due to COVID-19 occurred in autumn. A significant territorial diversification of hospitalized morbidity and in-hospital mortality was demonstrated, the worst situation concerned densely populated poviats and cities. Worse prognosis applied to older patients with comorbidities.

Factors Possibly Associated with Mortality in Intubated COVID-19 Patients: A Retrospective Study.

In Mexico, there is a high mortality rate among patients intubated because of COVID-19. The objective of this study was to investigate the associations of age, comorbidities, and biochemical parameters with the in-hospital mortality of COVID-19 patients. A retrospective study of 79 intubated patients admitted to one hospital in Jalisco, Mexico, between July 2020 and January 2021 was performed. Demographic and clinical characteristics were collected. The mean age was 57.7 (±12.8) years, with 83.5% non-survivors and 16.5% survivors. Age, lactate dehydrogenase (LDH) and D-dimer levels were found to be significantly higher in the non-survivor group (p = 0.011, p = 0.026, p = 0.007, respectively). Patients ≥58 years had a high risk of mortality (OR = 7.017). Significant correlations were also found in some of the study variables: LDH levels and leukocyte count (r = 0.388, p = 0.034) and CRP levels and fibrinogen (r = 0.692, p ˂ 0.001) in the patients ˂58 years. Leukocyte count with LDH levels (r = 0.381, p = 0.024) were significant in the group ≥58 years. No significant difference was observed in the presence of diabetes mellitus (DM) and hypertension. In conclusion, according to logistic regression analysis, age over 58 years represents the main factor associated with mortality in these patients.

Short-term impact of COVID-19 infection on right ventricular functions: single center observational study

BackgroundCOVID-19 pandemic is associated with high morbidity and mortality. Cardiovascular insult is a leading cause of in-hospital mortality in COVID-19 patients, especially right ventricular (RV) dysfunction and massive pulmonary embolism. This study aims to assess short-term impact of COVID 19 infection on (RV) functions among hospitalized patients with moderate or severe illness using bed side trans-thoracic echocardiogram. This study was conducted in 3 isolation hospitals in Cairo, spanning over 3 months during the expected pandemic peak in Egypt in 2020. The study recruited 100 consecutive patients with moderate or severe COVID-19 infection. Four patients refused to participate in the study. Patients with pre-existing structural heart diseases were excluded. All patients underwent full history taking and clinical examination. Bed side echocardiography was done emphasizing on (RV), and (RA) dimensions, (LV) functions and pulmonary artery systolic pressure (PSAS). Cardiac biomarkers were withdrawn and CT angiography was ordered when clinically warranted.ResultsThe mean age of the studied cohort was 59.5 ± 8.6 years with males comprising 71.9% of the studied group. (RV) and (RA) dilatation was noted in 8 cases (8.3%). (LV) dysfunction was noted in 11 cases (11.4%). (PASP) showed a statistically significant negative correlation with (LV) function. However, (PSAP) was positively correlated to (RA) and (RV) dimensions, tricuspid regurgitation (TR) jet severity, previous COVID infection and elevated cardiac biomarkers. Mortality was noted in 3 cases (3.1%), all had LV dysfunction with elevated troponin level. Six patients (6.2%) had combined (LV) and (RV) dysfunction.ConclusionsCOVID-19 illness had a negative impact on (RV) and (LV) functions, that could be assessed accurately by trans-thoracic 2 D echocardiogram. The degree of ventricular dysfunction correlated with the rise in cardiac biomarkers as well as the degree of (PASP).

Mortality due to COVID-19 during the pandemic: A comparison of first, second and third SMAtteo COvid19 REgistry (SMACORE)

COVID-19 tide had shattered on European countries with three distinct and tough waves, from March and April, 2020; October and November, 2020 and March and April, 2021 respectively. We observed a 50% reduction in the hazard of death during both wave II and III compared with wave I (HR 0.54, 95%CI 0.39–0.74 and HR 0.57, 95%CI 0.41–0.80, respectively). Sex and age were independent predictors of death. We compare in-hospital mortality of COVID-19 patients admitted at our Referral Hospital of Northern Italy during the different waves, discuss the reasons of the observed differences and suggest approaches to the challenges ahead.

Can we reliably automate clinical prognostic modelling? A retrospective cohort study for ICU triage prediction of in-hospital mortality of COVID-19 patients in the Netherlands

BackgroundBuilding Machine Learning (ML) models in healthcare may suffer from time-consuming and potentially biased pre-selection of predictors by hand that can result in limited or trivial selection of suitable models. We aimed to assess the predictive performance of automating the process of building ML models (AutoML) in-hospital mortality prediction modelling of triage COVID-19 patients at ICU admission versus expert-based predictor pre-selection followed by logistic regression. MethodsWe conducted an observational study of all COVID-19 patients admitted to Dutch ICUs between February and July 2020. We included 2,690 COVID-19 patients from 70 ICUs participating in the Dutch National Intensive Care Evaluation (NICE) registry. The main outcome measure was in-hospital mortality. We asessed model performance (at admission and after 24h, respectively) of AutoML compared to the more traditional approach of predictor pre-selection and logistic regression. FindingsPredictive performance of the autoML models with variables available at admission shows fair discrimination (average AUROC = 0·75-0·76 (sdev = 0·03), PPV = 0·70-0·76 (sdev = 0·1) at cut-off = 0·3 (the observed mortality rate), and good calibration. This performance is on par with a logistic regression model with selection of patient variables by three experts (average AUROC = 0·78 (sdev = 0·03) and PPV = 0·79 (sdev = 0·2)). Extending the models with variables that are available at 24h after admission resulted in models with higher predictive performance (average AUROC = 0·77-0·79 (sdev = 0·03) and PPV = 0·79-0·80 (sdev = 0·10-0·17)). ConclusionsAutoML delivers prediction models with fair discriminatory performance, and good calibration and accuracy, which is as good as regression models with expert-based predictor pre-selection. In the context of the restricted availability of data in an ICU quality registry, extending the models with variables that are available at 24h after admission showed small (but significantly) performance increase.

Potentially inappropriate medications based on TIME criteria and risk of in-hospital mortality in COVID-19 patients.

This study aimed to evaluate the relationship between hospital admission potentially inappropriate medications use (PIM) and in-hospital mortality of COVID-19, considering other possible factors related to mortality. The Turkish inappropriate medication use in the elderly (TIME) criteria were used to define PIM. The primary outcome of this study was in-hospital mortality. We included 201 older adults (mean age 73.1±9.4, 48.9% females). The in-hospital mortality rate and prevalence of PIM were 18.9% (n=38) and 96% (n=193), respectively. The most common PIM according to TIME to START was insufficient vitamin D and/or calcium intake per day. Proton-pump inhibitor use for multiple drug indications was the most prevalent PIM based on TIME to STOP findings. Mortality was related to PIM in univariate analysis (p=0.005) but not in multivariate analysis (p=0.599). Older age (hazards ratio (HR): 1.08; 95% confidence interval (CI): 1.02-1.13; p=0.005) and higher Nutritional Risk Screening 2002 (NRS-2002) scores were correlated with in-hospital mortality (HR: 1.29; 95%CI 1.00-1.65; p=0.042). Mortality was not associated with PIM. Older age and malnutrition were related to in-hospital mortality in COVID-19.

External Validation of 4C ISARIC Mortality Score in Critically ill COVID-19 Patients from Saudi Arabia.

Background:ISARIC mortality score is a risk stratification tool that helps predict the in-hospital mortality of COVID-19 patients. However, this tool was developed and validated in a British population, and thus, the external validation of this tool in local populations is important.Objectives:External validation of the ISARIC mortality score in COVID-19 patients from a large Saudi Arabian intensive care unit (ICU).Methods:This is a retrospective study that included all adult patients with COVID-19 admitted to the ICU of King Saud Medical City, Riyadh, Saudi Arabia, from March 2020 to June 2021. Patients who were pregnant or had pulmonary tuberculosis/human immunodeficiency virus were excluded along with patients with missing variables. Data were collected to calculate the ISARIC mortality score and then fitting receiver operator characteristic curve against patients’ outcome.Results:A total of 1493 critically ill COVID-19 patients were included. The mortality was 38%, the area under the curve of the score was 0.81 (95% confidence interval [CI]: 0.79–0.83, P < 0.001) and the cutoff value correctly classified 72.7% of the cohort. The cutoff value of >9 had sensitivity of 70.5% (95% CI: 66.6–74.3); specificity, 73.97% (95% CI: 71–76.8); positive predictive value, 62.4% (95% CI: 59.5–65.2) and negative predictive value, 80.2% (95% CI: 78.2–82.4).Conclusion:The ISARIC score was found to have excellent predictive ability for mortality in critically ill COVID-19 patients in our Saudi Arabian cohort. A cutoff score of >9 was the optimal criterion.

Mortality and Associated Risk Factors among In-Patients with Covid-19 in Douala, Cameroon: A Retrospective Cross-Sectional Study

Background: In March 2020, the World Health Organization declared the coronavirus disease 2019 a pandemic, this was caused by the Severe Acute Respiratory Syndrome Coronavirus 2. The overall mortality from this remains high, yet there are limited studies assessing the associated factors in Africa. Objective: To identify the factors associated with mortality in hospitalized COVID-19 patients in Douala, Cameroon. Method: We conducted a single-centre retrospective cross-sectional study by reviewing records of patients managed for COVID-19 between March and June 2020. Diagnosis was confirmed by real-time RT-PCR. Outcome of interest was mortality during hospitalization. We inputted and analyzed data using SPSS version 25.0, compared mortality between groups using the Chi’s squared test and the Fisher’s exact test where appropriately investigated for associations using the Logistic regression in a stepwise approach and alpha-value set at P = 0.05. Results: We analyzed 282 case files, 68% were males (M:F = 2:1) and a mean age of 52 (±15) years. We had an overall mortality of 31.5% (89/282 patients) and 54% (50/92 patients) admitted in the ICU died. Patients aged 0 - 39 years had a significantly lesser odds of mortality compared to those > 70 years (OR: 0.2 (0.1 - 0.5) P < 0.001). Hemodynamic instability at presentation showed association with mortality (P < 0.001) ranging as follows; moderate disease OR: 7.3 (2.4 - 21.8) versus Sepsis OR: 317 (58.3 - 1729.3). Mortality was as high as 63% in those with alveolar consolidation on CT scan (OR: 0.3 (0.1 - 0.6)), those with a 75% - 100% involvement of the lung parenchyma had a significantly higher mortality compared with those of <10% involvement (OR: 0.02 (0.01 - 0.06) P < 0.001). About 55% of patients placed on supplemental oxygen died (OR: 0.2 (0.1 - 0.3)) and up to 80% (OR: 0.1 (0.01 - 1.0)) of intubated patients died. In the ICU population, placement on non-invasive mechanical ventilation (AOR: 0.5 (0.2 - 1.2)) and intubation (AOR: 0.3 (0.03 - 2.6)) showed no significant differences in terms of mortality. Conclusion: The in-hospital mortality in COVID-19 patients is very high and hospitalisation into the ICU is associated with even higher mortality. Advanced age, diffused lung involvement (particularly with alveolar consolidation), hemodynamic instability at presentation and altered level of consciousness favoured hospitalisation in the ICU. Once in the ICU, placement on mechanical ventilation did not reduce death discernibly.

Is cardiac Troponin I Considered as A Predictor of In-hospital Mortality among COVID-19 Patients? A Retrospective Cohort Study.

Objective:To describe the levels of troponin I in COVID-19 patients and its role in the prediction of their in-hospital mortality as a cardiac biomarker.Methods:The current retrospective cohort study was performed on the clinical records of 649 COVID-19-related hospitalized cases with at leat one positive polymerase chain reaction (PCR) test in Tehran, Iran from February 2020 to early June 2020. The on admission troponin I level divided into two groups of ≤0.03ng/mL (normal) and >0.03ng/mL (abnormal). The adjusted COX-regression model was used to determine the relationship between the studied variables and patient’s in-hospital mortality.Results:In this study, the median age of subjects was 65 years (54.8% men) and 29.53% of them had abnormal troponin I levels. Besides, the in-hospital mortality rate among patients with abnormal troponin I levels was found to be 51.56%; whereas, patients with normal levels exhibited 18.82% mortality. Also, the multivariable analysis indicated that the risk of death among hospitalized COVID-19 patients displaying abnormal troponin I levels was 67% higher than those with normal troponin I levels (Hazard ratio=1.67, 95% confidence interval=1.08-2.56, p=0.019).Conclusion:It seems that troponin I is one of the important factors related to in-hospital mortality of COVID-19 patients. Next, due to the high prevalence of cardiac complications in these patients, it is highly suggested to monitor and control cardiac biomarkers along with other clinical factors upon the patient’s arrival at the hospital.