scholarly journals Risk factors for mortality among hospitalized patients with COVID-19

2020 ◽  
Author(s):  
Devin Incerti ◽  
Shemra Rizzo ◽  
Xiao Li ◽  
Lisa Lindsay ◽  
Vince Yau ◽  
...  
Keyword(s):  
Risk Factors ◽  
Vital Signs ◽  
Model Performance ◽  
Saturation Temperature ◽  
Prognostic Information ◽  
Good Discrimination ◽  
Improve Model ◽  
Laboratory Results ◽  
All Cause Mortality ◽  
Risk Patients

AbstractObjectivesTo develop a prognostic model to identify and quantify risk factors for mortality among patients admitted to the hospital with COVID-19.DesignRetrospective cohort study. Patients were randomly assigned to either training (80%) or test (20%) sets. The training set was used to fit a multivariable logistic regression. Predictors were ranked using variable importance metrics. Models were assessed by C-indices, Brier scores, and calibration plots in the test set.SettingOptum® de-identified COVID-19 Electronic Health Record dataset.Participants17,086 patients hospitalized with COVID-19 between February 20, 2020 and June 5, 2020.Main outcome measureAll-cause mortality during hospital stay.ResultsThe full model that included information on demographics, comorbidities, laboratory results and vital signs had good discrimination (C-index = 0.87) and was well calibrated, with some overpredictions for the most at-risk patients. Results were generally similar on the training and test sets, suggesting that there was little overfitting.Age was the most important risk factor. The performance of models that included all demographics and comorbidities (C-index = 0.79) was only slightly better than a model that only included age (C-index = 0.76). Across the study period, predicted mortality was 1.2% for 18-year olds, 8.4% for 55-year olds, and 28.6% for 85-year olds. Predicted mortality across all ages declined over the study period from 21.7% by March to 13.3% by May.ConclusionAge was the most important predictor of all-cause mortality although vital signs and laboratory results added considerable prognostic information with oxygen saturation, temperature, respiratory rate, lactate dehydrogenase, and white blood cell count being among the most important predictors. Demographic and comorbidity factors did not improve model performance appreciably. The model had good discrimination and was reasonably well calibrated, suggesting that it may be useful for assessment of prognosis.

scholarly journals Prognostic model to identify and quantify risk factors for mortality among hospitalised patients with COVID-19 in the USA

BMJ Open ◽  
2021 ◽  
Vol 11 (4) ◽  
pp. e047121
Author(s):  
Devin Incerti ◽  
Shemra Rizzo ◽  
Xiao Li ◽  
Lisa Lindsay ◽  
Vincent Yau ◽  
...  
Keyword(s):  
Risk Factors ◽  
Prognostic Model ◽  
Vital Signs ◽  
Saturation Temperature ◽  
Full Model ◽  
Good Discrimination ◽  
Hospitalised Patients ◽  
Laboratory Results ◽  
All Cause Mortality ◽  
The Usa

ObjectivesTo develop a prognostic model to identify and quantify risk factors for mortality among patients admitted to the hospital with COVID-19.DesignRetrospective cohort study. Patients were randomly assigned to either training (80%) or test (20%) sets. The training set was used to fit a multivariable logistic regression. Predictors were ranked using variable importance metrics. Models were assessed by C-indices, Brier scores and calibration plots in the test set.SettingOptum de-identified COVID-19 Electronic Health Record dataset including over 700 hospitals and 7000 clinics in the USA.Participants17 086 patients hospitalised with COVID-19 between 20 February 2020 and 5 June 2020.Main outcome measureAll-cause mortality while hospitalised.ResultsThe full model that included information on demographics, comorbidities, laboratory results, and vital signs had good discrimination (C-index=0.87) and was well calibrated, with some overpredictions for the most at-risk patients. Results were similar on the training and test sets, suggesting that there was little overfitting. Age was the most important risk factor. The performance of models that included all demographics and comorbidities (C-index=0.79) was only slightly better than a model that only included age (C-index=0.76). Across the study period, predicted mortality was 1.3% for patients aged 18 years old, 8.9% for 55 years old and 28.7% for 85 years old. Predicted mortality across all ages declined over the study period from 22.4% by March to 14.0% by May.ConclusionAge was the most important predictor of all-cause mortality, although vital signs and laboratory results added considerable prognostic information, with oxygen saturation, temperature, respiratory rate, lactate dehydrogenase and white cell count being among the most important predictors. Demographic and comorbidity factors did not improve model performance appreciably. The full model had good discrimination and was reasonably well calibrated, suggesting that it may be useful for assessment of prognosis.

scholarly journals The All-Cause Mortality and a Screening Tool to Determine High-Risk Patients among Prevalent Type 2 Diabetes Mellitus Patients

2018 ◽  
Vol 2018 ◽  
pp. 1-8 ◽  
Author(s):  
Mohamad Adam Bujang ◽  
Pei Xuan Kuan ◽  
Xun Ting Tiong ◽  
Fatin Ellisya Saperi ◽  
Mastura Ismail ◽  
...  
Keyword(s):  
Diabetes Mellitus ◽  
Risk Factors ◽  
Type 2 Diabetes ◽  
Type 2 Diabetes Mellitus ◽  
High Risk ◽  
Prognostic Model ◽  
Screening Tool ◽  
All Cause Mortality ◽  
Risk Patients

Aims. This study aims to determine the all-cause mortality and the associated risk factors for all-cause mortality among the prevalent type 2 diabetes mellitus (T2DM) patients within five years’ period and to develop a screening tool to determine high-risk patients. Methods. This is a cohort study of T2DM patients in the national diabetes registry, Malaysia. Patients’ particulars were derived from the database between 1st January 2009 and 31st December 2009. Their records were matched with the national death record at the end of year 2013 to determine the status after five years. The factors associated with mortality were investigated, and a prognostic model was developed based on logistic regression model. Results. There were 69,555 records analyzed. The mortality rate was 1.4 persons per 100 person-years. The major cause of death were diseases of the circulatory system (28.4%), infectious and parasitic diseases (19.7%), and respiratory system (16.0%). The risk factors of mortality within five years were age group (p<0.001), body mass index category (p<0.001), duration of diabetes (p<0.001), retinopathy (p=0.001), ischaemic heart disease (p<0.001), cerebrovascular (p=0.007), nephropathy (p=0.001), and foot problem (p=0.001). The sensitivity and specificity of the proposed model was fairly strong with 70.2% and 61.3%, respectively. Conclusions. The elderly and underweight T2DM patients with complications have higher risk for mortality within five years. The model has moderate accuracy; the prognostic model can be used as a screening tool to classify T2DM patients who are at higher risk for mortality within five years.

scholarly journals Development and external validation of prognostic models for COVID-19 to support risk stratification in secondary care

2021 ◽  
Author(s):  
Nicola J Adderley ◽  
Thomas Taverner ◽  
Malcolm Price ◽  
Christopher Sainsbury ◽  
David Greenwood ◽  
...  
Keyword(s):  
Secondary Care ◽  
External Validation ◽  
Model Performance ◽  
Demographic Variables ◽  
Prognostic Models ◽  
Physiological Measures ◽  
Good Discrimination ◽  
Improve Model ◽  
Wide Range ◽  
Improve Model Performance

AbstractObjectivesExisting UK prognostic models for patients admitted to hospital with COVID-19 are limited by reliance on comorbidities, which are under-recorded in secondary care, and lack of imaging data among the candidate predictors. Our aims were to develop and externally validate novel prognostic models for adverse outcomes (death, intensive therapy unit (ITU) admission) in UK secondary care; and externally validate the existing 4C score.DesignCandidate predictors included demographic variables, symptoms, physiological measures, imaging, laboratory tests. Final models used logistic regression with stepwise selection.SettingModel development was performed in data from University Hospitals Birmingham (UHB). External validation was performed in the CovidCollab dataset.ParticipantsPatients with COVID-19 admitted to UHB January-August 2020 were included.Main outcome measuresDeath and ITU admission within 28 days of admission.Results1040 patients with COVID-19 were included in the derivation cohort; 288 (28%) died and 183 (18%) were admitted to ITU within 28 days of admission. Area under the receiver operating curve (AUROC) for mortality was 0.791 (95%CI 0.761-0.822) in UHB and 0.767 (95%CI 0.754-0.780) in CovidCollab; AUROC for ITU admission was 0.906 (95%CI 0.883-0.929) in UHB and 0.811 (95%CI 0.795-0.828) in CovidCollab. Models showed good calibration. Addition of comorbidities to candidate predictors did not improve model performance. AUROC for the 4C score in the UHB dataset was 0.754 (95%CI 0.721-0.786).ConclusionsThe novel prognostic models showed good discrimination and calibration in derivation and external validation datasets, and outperformed the existing 4C score. The models can be integrated into electronic medical records systems to calculate each individual patient’s probability of death or ITU admission at the time of hospital admission. Implementation of the models and clinical utility should be evaluated.Article SummaryStrengths and limitations of this studyWe developed novel prognostic models predicting mortality and ITU admission within 28 days of admission for patients hospitalised with COVID-19, using a large routinely collected dataset gathered at admission with a wide range of possible predictors (demographic variables, symptoms, physiological measures, imaging, laboratory test results).These novel models showed good discrimination and calibration in both derivation and external validation cohorts, and outperformed the existing ISARIC model and 4C score in the derivation dataset. We found that addition of comorbidities to the set of candidate predictors included in model derivation did not improve model performance.If integrated into hospital electronic medical records systems, the model algorithms will provide a predicted probability of mortality or ITU admission for each patient based on their individual data at, or close to, the time of admission, which will support clinicians’ decision making with regard to appropriate patient care pathways and triage. This information might also assist clinicians in explaining complex prognostic assessments and decisions to patients and their relatives.A limitation of the study was that in the external validation cohort we were unable to examine all of the predictors included in the original full UHB model due to only a reduced set of candidate predictors being available in CovidCollab. Nevertheless, the reduced model performed well and the results suggest it may be applicable in a wide range of datasets where only a reduced set of predictor variables is available.Furthermore, it was not possible to carry out stratified analysis by ethnicity as the UHB dataset contained too few patients in most of the strata, and no ethnicity data was available in the CovidCollab dataset.

scholarly journals Adjusting Early Warning Score by clinical assessment: a study protocol for a Danish cluster-randomised, multicentre study of an Individual Early Warning Score (I-EWS)

BMJ Open ◽  
2020 ◽  
Vol 10 (1) ◽  
pp. e033676
Author(s):  
Pernille B Nielsen ◽  
Martin Schultz ◽  
Caroline Sophie Langkjaer ◽  
Anne Marie Kodal ◽  
Niels Egholm Pedersen ◽  
...  
Keyword(s):  
Multicentre Study ◽  
Nursing Staff ◽  
Clinical Assessment ◽  
Early Warning ◽  
Vital Signs ◽  
Early Warning Score ◽  
Prognostic Information ◽  
Use Of Resources ◽  
Cluster Randomised ◽  
All Cause Mortality

IntroductionTrack and trigger systems (TTSs) based on vital signs are implemented in hospitals worldwide to identify patients with clinical deterioration. TTSs may provide prognostic information but do not actively include clinical assessment, and their impact on severe adverse events remain uncertain. The demand for prospective, multicentre studies to demonstrate the effectiveness of TTSs has grown the last decade. Individual Early Warning Score (I-EWS) is a newly developed TTS with an aggregated score based on vital signs that can be adjusted according to the clinical assessment of the patient. The objective is to compare I-EWS with the existing National Early Warning Score (NEWS) algorithm regarding clinical outcomes and use of resources.Method and analysisIn a prospective, multicentre, cluster-randomised, crossover, non-inferiority study. Eight hospitals are randomised to use either NEWS in combination with the Capital Region of Denmark NEWS Override System (CROS) or implement I-EWS for 6.5 months, followed by a crossover. Based on their clinical assessment, the nursing staff can adjust the aggregated score with a maximum of −4 or +6 points. We expect to include 150 000 unique patients. The primary endpoint is all-cause mortality at 30 days. Coprimary endpoint is the average number of times per day a patient is NEWS/I-EWS-scored, and secondary outcomes are all-cause mortality at 48 hours and at 7 days as well as length of stay.Ethics and disseminationThe study was presented for the Regional Ethics committee who decided that no formal approval was needed according to Danish law (J.no. 1701733). The I-EWS study is a large prospective, randomised multicentre study that investigates the effect of integrating a clinical assessment performed by the nursing staff in a TTS, in a head-to-head comparison with the internationally used NEWS with the opportunity to use CROS.Trial registration numberNCT03690128.

scholarly journals Analysis of Prognostic Relevance based on Ambulatory Vital Signs Among Pregnancy with Pulmonary Hypertension in the ICU

2021 ◽  
Author(s):  
Renhuai Liu ◽  
Ziyu Zheng ◽  
Chen He ◽  
Chong Lei ◽  
Yu Chen ◽  
...  
Keyword(s):  
Blood Pressure ◽  
Heart Rate ◽  
Pulmonary Hypertension ◽  
Cox Regression ◽  
Vital Signs ◽  
Model Performance ◽  
Mean Blood Pressure ◽  
Dynamic Features ◽  
Prognostic Information ◽  
Maternal Deaths

Abstract Purpose: The study seeks to utilise the extensively monitored data to explore the prognostic information in the continuous ambulatory vital signs of the pregnant women with pulmonary hypertension in the ICU, aiming to bring insights to physicians on evaluation and management of these specific patients. Methods: This is a retrospective study of consecutive obstetric patients with PH admitted to ICU of the First Affiliated Hospital of Air Force Military Medical University of China, from January 2011 to May 2020. 92 cases are analysed via time-dependent Cox regression to take account of the dynamic features of vital signs. Results: Seven out of 92 maternal deaths occurred, with most maternal deaths occurring within the first three days of admission to the ICU. The vital signs for the survived are more stable and normally ranged comparing to the death. Three vital signs are identified as risk factors in the maternal in-hospital mortality model: SpO 2 (OR,0.93;95%CI,0.89-0.98), Heart rate(OR, 0.95 ; 95%CI, 0.92-0.99), Mean blood pressure(OR, 1.1 ; 95%CI, 0.98-1). The model performance is justified by the ROC curve with AUC being 0.84. Further exploration showed that the total and the longest consecutive time ratios also affect the outcome. Conclusions: Pregnancy women with PH who dead in hospital experienced long-term abnormal fluctuations in blood pressure, heart rate and blood oxygen during ICU stay. Both dynamic and time ratios reported impacts relating to SpO 2 , heart rate and blood pressure. In general SpO 2 mitigate the hazard. Effects of the heart rate and mean blood pressure should be described combining the time ratios of hypotension and tachycardia.

Behavioral Emergencies and Crises

2010 ◽  
pp. 739-762
Author(s):  
Phillip M. Kleespies ◽  
Justin M. Hill
Keyword(s):  
Mental Health ◽  
Risk Factors ◽  
High Risk ◽  
Work Environment ◽  
Emotional Impact ◽  
High Risk Patients ◽  
Risk Patients ◽  
Life Threatening ◽  
Behavioral Emergencies ◽  
And Training

This chapter illustrates the mental health clinician’s relationship with behavioral emergencies. The chapter begins by distinguishing the terms behavioral emergency and behavioral crisis, and underlying themes among all behavioral emergencies are identified. Given that most clinicians will face a behavioral emergency in their careers, the importance of enhancing the process of educating and training practitioners for such situations far beyond the minimal training that currently exists is highlighted. The chapter continues by exploring various aspects of evaluating and managing high-risk patients (i.e., those who exhibit violent tendencies toward themselves or others, and those at risk for victimization). It includes a discussion of the benefits and limitations to estimating life-threatening risk factors and specific protective factors. The chapter concludes by discussing the emotional impact that working with high-risk patients has on clinicians, and an emphasis is placed on the importance of creating a supportive work environment.

scholarly journals Risk factors for COVID-19 diagnosis, hospitalization, and subsequent all-cause mortality in Sweden: a nationwide study

2021 ◽  
Vol 36 (3) ◽  
pp. 287-298
Author(s):  
Jonathan Bergman ◽  
Marcel Ballin ◽  
Anna Nordström ◽  
Peter Nordström
Keyword(s):  
Risk Factors ◽  
Down Syndrome ◽  
Long Term Care ◽  
Care Facility ◽  
Prescription Medication ◽  
Term Care ◽  
Term Care Facility ◽  
Long Term Care Facility ◽  
All Cause Mortality

AbstractWe conducted a nationwide, registry-based study to investigate the importance of 34 potential risk factors for coronavirus disease 2019 (COVID-19) diagnosis, hospitalization (with or without intensive care unit [ICU] admission), and subsequent all-cause mortality. The study population comprised all COVID-19 cases confirmed in Sweden by mid-September 2020 (68,575 non-hospitalized, 2494 ICU hospitalized, and 13,589 non-ICU hospitalized) and 434,081 randomly sampled general-population controls. Older age was the strongest risk factor for hospitalization, although the odds of ICU hospitalization decreased after 60–69 years and, after controlling for other risk factors, the odds of non-ICU hospitalization showed no trend after 40–49 years. Residence in a long-term care facility was associated with non-ICU hospitalization. Male sex and the presence of at least one investigated comorbidity or prescription medication were associated with both ICU and non-ICU hospitalization. Three comorbidities associated with both ICU and non-ICU hospitalization were asthma, hypertension, and Down syndrome. History of cancer was not associated with COVID-19 hospitalization, but cancer in the past year was associated with non-ICU hospitalization, after controlling for other risk factors. Cardiovascular disease was weakly associated with non-ICU hospitalization for COVID-19, but not with ICU hospitalization, after adjustment for other risk factors. Excess mortality was observed in both hospitalized and non-hospitalized COVID-19 cases. These results confirm that severe COVID-19 is related to age, sex, and comorbidity in general. The study provides new evidence that hypertension, asthma, Down syndrome, and residence in a long-term care facility are associated with severe COVID-19.

TELECOVID: Remote Vital Signs Monitoring of COVID-19 Risk Patients in Home Isolation With an In-Ear Wearable

2021 ◽  
Vol 20 (2) ◽  
pp. 58-62
Author(s):  
Melanie Baldinger ◽  
Axel Heinrich ◽  
Tim Adams ◽  
Eimo Martens ◽  
Michael Dommasch ◽  
...  
Keyword(s):  
Vital Signs ◽  
Vital Signs Monitoring ◽  
Risk Patients

scholarly journals Long-term survival after Carpentier-Edwards Perimount aortic valve replacement in Western Denmark: a multi-centre observational study

2021 ◽  
Vol 16 (1) ◽  
Author(s):  
Lytfi Krasniqi ◽  
Mads P. Kronby ◽  
Lars P. S. Riber
Keyword(s):  
Risk Factors ◽  
Aortic Valve ◽  
Aortic Valve Replacement ◽  
Valve Replacement ◽  
Significant Risk ◽  
Valve Prosthesis ◽  
Term Survival ◽  
Long Term Survival ◽  
All Cause Mortality

Abstract Background This study describes the long-term survival, risk of reoperation and clinical outcomes of patients undergoing solitary surgical aortic valve replacement (SAVR) with a Carpentier-Edwards Perimount (CE-P) bioprosthetic in Western Denmark. The renewed interest in SAVR is based on the questioning regarding the long-term survival since new aortic replacement technique such as transcatheter aortic-valve replacement (TAVR) probably have shorter durability, why assessment of long-term survival could be a key issue for patients. Methods From November 1999 to November 2013 a cohort of a total of 1604 patients with a median age of 73 years (IQR: 69–78) undergoing solitary SAVR with CE-P in Western Denmark was obtained November 2018 from the Western Danish Heart Registry (WDHR). The primary endpoint was long-term survival from all-cause mortality. Secondary endpoints were survival free from major adverse cardiovascular and cerebral events (MACCE), risk of reoperation, cause of late death, patient-prothesis mismatch, risk of AMI, stroke, pacemaker or ICD implantation and postoperative atrial fibrillation (POAF). Time-to-event analysis was performed with Kaplan-Meier curve, cumulative incidence function was performed with Nelson-Aalen cumulative hazard estimates. Cox regression was applied to detect risk factors for death and reoperation. Results In-hospital mortality was 2.7% and 30-day mortality at 3.4%. The 5-, 10- and 15-year survival from all-cause mortality was 77, 52 and 24%, respectively. Survival without MACCE was 80% after 10 years. Significant risk factors of mortality were small valves, smoking and EuroSCORE II ≥4%. The risk of reoperation was < 5% after 7.5 years and significant risk factors were valve prosthesis-patient mismatch and EuroSCORE II ≥4%. Conclusions Patients undergoing aortic valve replacement with a Carpentier-Edwards Perimount valve shows a very satisfying long-term survival. Future research should aim to investigate biological valves long-term durability for comparison of different SAVR to different TAVR in long perspective.

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