scholarly journals Development of a multivariable model predicting COVID-19 mortality risk from comorbidities in an Italian cohort of 18,286 confirmed cases aged 40 years or older

2020 ◽  
Author(s):  
Anita Andreano ◽  
Rossella Murtas ◽  
Sara Tunesi ◽  
Maria Teresa Greco ◽  
David Consolazio ◽  
...  
Keyword(s):  
High Risk ◽  
Mortality Risk ◽  
Chronic Conditions ◽  
External Validation ◽  
Administrative Database ◽  
Multivariable Logistic Regression Model ◽  
Risk Of Death ◽  
And Gender ◽  
High Risk Subject ◽  
Italian Cohort

Abstract Background: large studies on the predictive role of chronic conditions on mortality from COVID‑19 are scarce. We developed a predictive model of death from COVID‑19 in an Italian cohort aged 40 years or older.Methods: we conducted a cohort study on prospectively collected data. The cohort included all (n=18,286) swab positive cases ≥40 year-old in patients registered with the Agency for Health Protection (AHP) of Milan up to 27/04/2020. Data on comorbidities were obtained from the chronic condition administrative database of the AHP. A multivariable logistic regression model, including age and gender and the selected conditions, was fitted to predict 30-day mortality risk and internally validated. External validation and recalibration were performed in a cohort of untested subjects with COVID-19 like symptoms. R software was used for the analysis.Results: chronic conditions having the largest model-adjusted odds ratio (OR) of dying within 30 days from COVID-19 infection were chronic heart failure (OR=1.9, 95%CI 1.5-2.5), tumors (OR=1.8, 95%CI 1.4-2.3), complicated diabetes (OR=1.6, 95%CI 1.1-2.2) and dialysis-dependent chronic kidney disease (OR=1.5, 95%CI 1.0-2.2). Bootstrap-validated c-index was 0.78. The model fitted on the validation cohort had a c-index of 0.93, but required recalibration. With this latter model, at a 10% risk of death threshold, 11% of the AHP population aged 40 years or older is considered at high risk.Conclusion: we identified a selected number of comorbidities predicting early risk of death in a large COVID-19 cohort aged 40 years or older. In a new epidemic wave, our results will help physicians and health systems to identify high-risk subject to target for prevention and therapy in this specific age group.

scholarly journals Establishment of a novel scoring model for mortality risk prediction in HIV-infected patients with cryptococcal meningitis

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Ting Zhao ◽  
Xiao-Lei Xu ◽  
Jing-Min Nie ◽  
Xiao-Hong Chen ◽  
Zhong-Sheng Jiang ◽  
...  
Keyword(s):  
High Risk ◽  
Risk Prediction ◽  
Mortality Risk ◽  
Cryptococcal Meningitis ◽  
External Validation ◽  
Area Under The Curve ◽  
Multicenter Cohort Study ◽  
Internal Validation ◽  
Scoring Model ◽  
Risk Of Death

Abstract Background Cryptococcal meningitis (CM) remains a leading cause of death in HIV-infected patients, despite advances in CM diagnostic and therapeutic strategies. This study was performed with the aim to develop and validate a novel scoring model to predict mortality risk in HIV-infected patients with CM (HIV/CM). Methods Data on HIV/CM inpatients were obtained from a Multicenter Cohort study in China. Independent risk factors associated with mortality were identified based on data from 2013 to 2017, and a novel scoring model for mortality risk prediction was established. The bootstrapping statistical method was used for internal validation. External validation was performed using data from 2018 to 2020. Results We found that six predictors, including age, stiff neck, impaired consciousness, intracranial pressure, CD4+ T-cell count, and urea levels, were associated with poor prognosis in HIV/CM patients. The novel scoring model could effectively identify HIV/CM patients at high risk of death on admission (area under curve 0.876; p<0.001). When the cut-off value of 5.5 points or more was applied, the sensitivity and specificity was 74.1 and 83.8%, respectively. Our scoring model showed a good discriminatory ability, with an area under the curve of 0.879 for internal validation via bootstrapping, and an area under the curve of 0.886 for external validation. Conclusions Our developed scoring model of six variables is simple, convenient, and accurate for screening high-risk patients with HIV/CM, which may be a useful tool for physicians to assess prognosis in HIV/CM inpatients.

Renal function improves mortality prediction in acute pulmonary embolism: results of a multicentre cohort study with external validation in the RIETE registry

2020 ◽  
Vol 41 (Supplement_2) ◽  
Author(s):  
R Chopard ◽  
D Jimenez ◽  
G Serzian ◽  
F Ecarnot ◽  
N Falvo ◽  
...  
Keyword(s):  
Pulmonary Embolism ◽  
Renal Function ◽  
High Risk ◽  
Renal Dysfunction ◽  
External Validation ◽  
Mortality Prediction ◽  
Model Fit ◽  
Funding Source ◽  
Risk Of Death ◽  
Acute Pe

Abstract Background Renal dysfunction may influence outcomes after pulmonary embolism (PE). We determined the incremental value of adding renal function impairment (estimated glomerular filtration rate, eGFR &lt;60 ml/min/1.73m2) on top of the 2019 ESC prognostic model, for the prediction of 30-day all-cause mortality in acute PE patients from a prospective, multicenter cohort. Methods and results We identified which of three eGFR formulae predicted death most accurately. Changes in global model fit, discrimination, calibration and net reclassification index (NRI) were evaluated with addition of eGFR. We prospectively included consecutive adult patients with acute PE diagnosed as per ESC guidelines. Among 1,943 patients, (mean age 67.3±17.1, 50.4% women), 107 (5.5% (95% CI 4.5–6.5%)) died during 30-day follow-up. The eGFRMDRD4 formula was the most accurate for prediction of death. The observed mortality rate was higher for intermediate-low risk (OR 1.8, 95% CI 1.1–3.4) and high-risk PE (OR 10.3, 95% CI 3.6–17.3), and 30-day bleeding was significantly higher (OR 2.1, 95% CI 1.3–3.5) in patients with vs without eGFRMDRD4 &lt;60 ml/min/1.73m2. The addition of eGFRMDRD4 information improved model fit, discriminatory capacity, and calibration of the ESC models. NRI was significantly improved (p&lt;0.001), with 18% reclassification of predicted mortality, specifically in intermediate and high-risk PE. External validation using data from the RIETE registry confirmed our findings (Table). Conclusion Addition of eGFRMDRD4-derived renal dysfunction on top of the ESC prognostic algorithm yields significant reclassification of risk of death in intermediate and high-risk PE. Impact on therapy remains to be determined. Funding Acknowledgement Type of funding source: Private grant(s) and/or Sponsorship. Main funding source(s): BMS-Pfizer Alliance, Bayer Healthcare

scholarly journals Comparison of Cancer Patients to Non-Cancer Patients among COVID-19 Inpatients at a National Level

Cancers ◽  
2021 ◽  
Vol 13 (6) ◽  
pp. 1436
Author(s):  
Alain Bernard ◽  
Jonathan Cottenet ◽  
Philippe Bonniaud ◽  
Lionel Piroth ◽  
Patrick Arveux ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Cancer Patients ◽  
Mortality Risk ◽  
Hospital Admissions ◽  
National Level ◽  
Administrative Database ◽  
Hospital Death ◽  
Solid Cancer ◽  
Hematological Cancer ◽  
Risk Of Death

(1) Background: Several smaller studies have shown that COVID-19 patients with cancer are at a significantly higher risk of death. Our objective was to compare patients hospitalized for COVID-19 with cancer to those without cancer using national data and to study the effect of cancer on the risk of hospital death and intensive care unit (ICU) admission. (2) Methods: All patients hospitalized in France for COVID-19 in March–April 2020 were included from the French national administrative database, which contains discharge summaries for all hospital admissions in France. Cancer patients were identified within this population. The effect of cancer was estimated with logistic regression, adjusting for age, sex and comorbidities. (3) Results: Among the 89,530 COVID-19 patients, we identified 6201 cancer patients (6.9%). These patients were older and were more likely to be men and to have complications (acute respiratory and kidney failure, venous thrombosis, atrial fibrillation) than those without cancer. In patients with hematological cancer, admission to ICU was significantly more frequent (24.8%) than patients without cancer (16.4%) (p < 0.01). Solid cancer patients without metastasis had a significantly higher mortality risk than patients without cancer (aOR = 1.4 [1.3–1.5]), and the difference was even more marked for metastatic solid cancer patients (aOR = 3.6 [3.2–4.0]). Compared to patients with colorectal cancer, patients with lung cancer, digestive cancer (excluding colorectal cancer) and hematological cancer had a higher mortality risk (aOR = 2.0 [1.6–2.6], 1.6 [1.3–2.1] and 1.4 [1.1–1.8], respectively). (4) Conclusions: This study shows that, in France, patients with COVID-19 and cancer have a two-fold risk of death when compared to COVID-19 patients without cancer. We suggest the need to reorganize facilities to prevent the contamination of patients being treated for cancer, similar to what is already being done in some countries.

scholarly journals Establishment of a novel scoring model for mortality risk prediction in HIV-infected patients with cryptococcal meningitis

2021 ◽  
Author(s):  
Ting Zhao ◽  
Xiao-Lei Xu ◽  
Jing-Min Nie ◽  
Xiao-Hong Chen ◽  
Zhong-Sheng Jiang ◽  
...  
Keyword(s):  
High Risk ◽  
Risk Prediction ◽  
Mortality Risk ◽  
Cryptococcal Meningitis ◽  
Area Under Curve ◽  
The Novel ◽  
Multicenter Cohort Study ◽  
Scoring Model ◽  
Risk Of Death ◽  
Prediction Probability

Abstract Purpose: Cryptococcal meningitis (CM) remains a leading cause of death in HIV-infected patients, despite advances in CM diagnostic and therapeutic strategies. This study was performed with the aim to develop and validate a novel scoring model to predict mortality risk in HIV-infected patients with CM (HIV/CM).Methods: Data on HIV/CM inpatients were obtained from a Multicenter Cohort study in China. Independent risk factors associated with mortality were identified based on data from 2013 to 2017, and a novel scoring model for mortality risk prediction was established. The prediction probability of the novel model was evaluated and verified using data from 2018 to 2020.Results: We found that six predictors, including age, stiff neck, impaired consciousness, intracranial pressure, CD4+ T-cell count, and urea levels, were associated with poor prognosis in HIV/CM patients. The novel scoring model could effectively identify HIV/CM patients at high risk of death on admission (area under curve 0.876; p＜0.001). When the cut-off value of 5.5 points or more was applied, the sensitivity and specificity was 74.1% and 83.8%, respectively. Additionally, our scoring model showed a good discriminatory ability in the validation cohort (area under curve 0·886; p＜0.001).Conclusions: Our developed scoring model of six variables is simple, convenient, and accurate for screening high-risk patients with HIV/CM, which may be a useful tool for physicians to assess prognosis in HIV/CM inpatients.

scholarly journals Gene Expression Patterns Distinguish Mortality Risk in Patients with Postsurgical Shock

2020 ◽  
Vol 9 (5) ◽  
pp. 1276
Author(s):  
Pedro Martínez-Paz ◽  
Marta Aragón-Camino ◽  
Esther Gómez-Sánchez ◽  
Mario Lorenzo-López ◽  
Estefanía Gómez-Pesquera ◽  
...  
Keyword(s):  
Gene Expression ◽  
High Risk ◽  
Mortality Risk ◽  
Validation Cohort ◽  
Expression Patterns ◽  
Gene Expression Signature ◽  
Mrna Levels ◽  
Characteristic Analysis ◽  
Discovery Cohort ◽  
Risk Of Death

Nowadays, mortality rates in intensive care units are the highest of all hospital units. However, there is not a reliable prognostic system to predict the likelihood of death in patients with postsurgical shock. Thus, the aim of the present work is to obtain a gene expression signature to distinguish the low and high risk of death in postsurgical shock patients. In this sense, mRNA levels were evaluated by microarray on a discovery cohort to select the most differentially expressed genes between surviving and non-surviving groups 30 days after the operation. Selected genes were evaluated by quantitative real-time polymerase chain reaction (qPCR) in a validation cohort to validate the reliability of data. A receiver-operating characteristic analysis with the area under the curve was performed to quantify the sensitivity and specificity for gene expression levels, which were compared with predictions by established risk scales, such as acute physiology and chronic health evaluation (APACHE) and sequential organ failure assessment (SOFA). IL1R2, CD177, RETN, and OLFM4 genes were upregulated in the non-surviving group of the discovery cohort, and their predictive power was confirmed in the validation cohort. This work offers new biomarkers based on transcriptional patterns to classify the postsurgical shock patients according to low and high risk of death. The results present more accuracy than other mortality risk scores.

scholarly journals Simple Scoring Tool to Estimate Risk of Hospitalization and Mortality in Ambulatory and Emergency Department Patients with COVID-19

2021 ◽  
Author(s):  
Brandon J. Webb ◽  
Nicholas M. Levin ◽  
Nancy Grisel ◽  
Samuel M. Brown ◽  
Ithan D. Peltan ◽  
...  
Keyword(s):  
Monoclonal Antibodies ◽  
High Risk ◽  
Validation Cohort ◽  
Characteristic Curve ◽  
External Validation ◽  
Receiver Operator Characteristic Curve ◽  
Multivariable Logistic Regression Model ◽  
White Race ◽  
Derivation Cohort ◽  
Scoring Tool

AbstractBackgroundAccurate methods of identifying patients with COVID-19 who are at high risk of poor outcomes has become especially important with the advent of limited-availability therapies such as monoclonal antibodies. Here we describe development and validation of a simple but accurate scoring tool to classify risk of hospitalization and mortality.MethodsAll consecutive patients testing positive for SARS-CoV-2 from March 25-October 1, 2020 within the Intermountain Healthcare system were included. The cohort was randomly divided into 70% derivation and 30% validation cohorts. A multivariable logistic regression model was fitted for 14-day hospitalization. The optimal model was then adapted to a simple, probabilistic score and applied to the validation cohort and evaluated for prediction of hospitalization and 28-day mortality.Results22,816 patients were included; mean age was 40 years, 50.1% were female and 44% identified as non-white race or Hispanic/Latinx ethnicity. 6.2% required hospitalization and 0.4% died. Criteria in the simple model included: age (0.5 points per decade); high-risk comorbidities (2 points each): diabetes mellitus, severe immunocompromised status and obesity (body mass index≥30); non-white race/Hispanic or Latinx ethnicity (2 points), and 1 point each for: male sex, dyspnea, hypertension, coronary artery disease, cardiac arrythmia, congestive heart failure, chronic kidney disease, chronic pulmonary disease, chronic liver disease, cerebrovascular disease, and chronic neurologic disease. In the derivation cohort (n=16,030) area under the receiver-operator characteristic curve (AUROC) was 0.82 (95% CI 0.81-0.84) for hospitalization and 0.91 (0.83-0.94) for 28-day mortality; in the validation cohort (n=6,786) AUROC for hospitalization was 0.8 (CI 0.78-0.82) and for mortality 0.8 (CI 0.69-0.9).ConclusionA prediction score based on widely available patient attributes accurately risk stratifies patients with COVID-19 at the time of testing. Applications include patient selection for therapies targeted at preventing disease progression in non-hospitalized patients, including monoclonal antibodies. External validation in independent healthcare environments is needed.

scholarly journals Association of depression and gender with mortality in old age

2000 ◽  
Vol 177 (4) ◽  
pp. 336-342 ◽  
Author(s):  
R. A. Schoevers ◽  
M. I. Geerlings ◽  
A. T. F. Beekman ◽  
B. W. J. H. Penninx ◽  
D. J. H. Deeg ◽  
...  
Keyword(s):  
Mortality Risk ◽  
Older Persons ◽  
Excess Mortality ◽  
Psychotic Depression ◽  
Mild Depression ◽  
Men And Women ◽  
Risk Of Death ◽  
And Gender ◽  
Depressive Syndromes

BackgroundThe association between depression and increased mortality risk in older persons may depend on the severity of the depressive disorder and gender.AimsTo investigate the association between major and mild depressive syndromes and excess mortality in community-living elderly men and women.MethodDepression (Geriatric Mental State AGECAT) was assessed in 4051 older persons, with a 6-year follow-up of community death registers. The mortality risk of neurotic and psychotic depression was calculated after adjustment for demographic variables, physical illness, cognitive decline and functional disabilities.ResultsA total of 75% of men and 41% of women with psychotic depression had died at follow-up. Psychotic depression was associated with significant excess mortality in both men and women. Neurotic depression was associated with a 1.67-fold higher mortality risk in men only.ConclusionsIn the elderly, major depressive syndromes increase the risk of death in both men and women, but mild depression increases the risk of death only in men.

scholarly journals Analysis of Systemic Inflammatory Factors and Survival Outcomes in Endometrial Cancer Patients Staged I-III FIGO and Treated with Postoperative External Radiotherapy

2020 ◽  
Vol 9 (5) ◽  
pp. 1441
Author(s):  
Katarzyna Holub ◽  
Fabio Busato ◽  
Sebastien Gouy ◽  
Roger Sun ◽  
Patricia Pautier ◽  
...  
Keyword(s):  
Endometrial Cancer ◽  
High Risk ◽  
Cancer Patients ◽  
External Beam Radiotherapy ◽  
Univariate Analysis ◽  
External Validation ◽  
Inflammatory Factors ◽  
Lymphocyte Ratio ◽  
Risk Of Death ◽  
The Impact

Background: The causal link between elevated systemic inflammation biomarkers and poor survival has been demonstrated in cancer patients. However, the evidence for this correlation in endometrial cancer (EC) is too weak to influence current criteria of risk assessment. Here, we examined the role of inflammatory indicators as a tool to identify EC patients at higher risk of death in a retrospective observational study. Methods: A total of 155 patients surgically diagnosed with EC stage I-III FIGO 2009 and treated with postoperative External Beam Radiotherapy (EBRT) ± brachytherapy and chemotherapy according to ESMO-ESTRO-ESGO recommendation for patients at high risk of recurrence at the Gustave Roussy Institut, France, and Hospital Clínic, Spain, between 2008 and 2017 were evaluated. The impact of pre-treatment Neutrophil-to-Lymphocyte Ratio (NLR ≥ 2.2), Monocyte-to-Lymphocyte Ratio (MLR ≥ 0.18), Systemic Immune-Inflammatory Index (SII ≥ 1100) and lymphopenia (<1.0×109/L) on overall survival (OS), cancer-specific survival and progression-free survival was evaluated. Subsequently, a cohort of 142 patients within high-advanced risk groups according to ESMO-ESGO-ESTRO classification was evaluated. Results: On univariate analysis, NLR (HR = 2.2, IC 95% 1.1–4.7), SII (HR = 2.2, IC 95% 1.1–4.6), MLR (HR = 5.0, IC 95% 1.1–20.8) and lymphopenia (HR = 3.8, IC 95% 1.6–9.0) were associated with decreased OS. On multivariate analysis, NLR, MLR, SII and lymphopenia proved to be independent unfavorable prognostic factors. Conclusions: lymphopenia and lymphocytes-related ratio are associated with poorer outcome in surgically staged I-III FIGO EC patients classified as high risk and treated with adjuvant EBRT and could be considered at cancer diagnosis. External validation in an independent cohort is required before implementation for patients’ stratification.

scholarly journals Geographically-targeted COVID-19 vaccination is more equitable than age-based thresholds alone

2021 ◽  
Author(s):  
Elizabeth Wrigley-Field ◽  
Mathew V Kiang ◽  
Alicia R Riley ◽  
Magali Barbieri ◽  
Yea-Hung Chen ◽  
...  
Keyword(s):  
United States ◽  
High Risk ◽  
Mortality Risk ◽  
The United States ◽  
People Of Color ◽  
White People ◽  
Risk Of Death ◽  
Geographic Prioritization ◽  
Geographically Targeted ◽  
Racial Ethnic

AbstractCOVID-19 mortality increases dramatically with age and is also substantially higher among Black, Indigenous, and People of Color (BIPOC) populations in the United States. These two facts introduce tradeoffs because BIPOC populations are younger than white populations. In analyses of California and Minnesota--demographically divergent states--we show that COVID vaccination schedules based solely on age benefit the older white populations at the expense of younger BIPOC populations with higher risk of death from COVID-19. We find that strategies that prioritize high-risk geographic areas for vaccination at all ages better target mortality risk than age-based strategies alone, although they do not always perform as well as direct prioritization of high-risk racial/ethnic groups.One-sentence summaryAge-based COVID-19 vaccination prioritizes white people above higher-risk others; geographic prioritization improves equity.

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