Real-time prognostic biomarkers for predicting in-hospital mortality and cardiac complications in COVID-19 patients
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ABSTRACT:
SUBMITTER: Omar R
PROVIDER: S-EPMC10917247 | biostudies-literature | 2024 Jan
REPOSITORIES: biostudies-literature
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Project description:BackgroundCOVID-19 infection carries significant morbidity and mortality. Current risk prediction for complications in COVID-19 is limited, and existing approaches fail to account for the dynamic course of the disease.ObjectivesThe purpose of this study was to develop and validate the COVID-HEART predictor, a novel continuously updating risk-prediction technology to forecast adverse events in hospitalized patients with COVID-19.MethodsRetrospective registry data from patients with severe acute respiratory syndrome coronavirus 2 infection admitted to 5 hospitals were used to train COVID-HEART to predict all-cause mortality/cardiac arrest (AM/CA) and imaging-confirmed thromboembolic events (TEs) (n = 2,550 and n = 1,854, respectively). To assess COVID-HEART's performance in the face of rapidly changing clinical treatment guidelines, an additional 1,100 and 796 patients, admitted after the completion of development data collection, were used for testing. Leave-hospital-out validation was performed.ResultsOver 20 iterations of temporally divided testing, the mean area under the receiver operating characteristic curve were 0.917 (95% confidence interval [CI]: 0.916-0.919) and 0.757 (95% CI: 0.751-0.763) for prediction of AM/CA and TE, respectively. The interquartile ranges of median early warning times were 14 to 21 hours for AM/CA and 12 to 60 hours for TE. The mean area under the receiver operating characteristic curve for the left-out hospitals were 0.956 (95% CI: 0.936-0.976) and 0.781 (95% CI: 0.642-0.919) for prediction of AM/CA and TE, respectively.ConclusionsThe continuously updating, fully interpretable COVID-HEART predictor accurately predicts AM/CA and TE within multiple time windows in hospitalized COVID-19 patients. In its current implementation, the predictor can facilitate practical, meaningful changes in patient triage and resource allocation by providing real-time risk scores for these outcomes. The potential utility of the predictor extends to COVID-19 patients after hospitalization and beyond COVID-19.
| S-EPMC9080121 | biostudies-literature
Project description:The prognostic power of circulating cardiac biomarkers, their utility, and pattern of release in coronavirus disease 2019 (COVID-19) patients have not been clearly defined. In this multicentered retrospective study, we enrolled 3219 patients with diagnosed COVID-19 admitted to 9 hospitals from December 31, 2019 to March 4, 2020, to estimate the associations and prognostic power of circulating cardiac injury markers with the poor outcomes of COVID-19. In the mixed-effects Cox model, after adjusting for age, sex, and comorbidities, the adjusted hazard ratio of 28-day mortality for hs-cTnI (high-sensitivity cardiac troponin I) was 7.12 ([95% CI, 4.60-11.03] P<0.001), (NT-pro)BNP (N-terminal pro-B-type natriuretic peptide or brain natriuretic peptide) was 5.11 ([95% CI, 3.50-7.47] P<0.001), CK (creatine phosphokinase)-MB was 4.86 ([95% CI, 3.33-7.09] P<0.001), MYO (myoglobin) was 4.50 ([95% CI, 3.18-6.36] P<0.001), and CK was 3.56 ([95% CI, 2.53-5.02] P<0.001). The cutoffs of those cardiac biomarkers for effective prognosis of 28-day mortality of COVID-19 were found to be much lower than for regular heart disease at about 19%-50% of the currently recommended thresholds. Patients with elevated cardiac injury markers above the newly established cutoffs were associated with significantly increased risk of COVID-19 death. In conclusion, cardiac biomarker elevations are significantly associated with 28-day death in patients with COVID-19. The prognostic cutoff values of these biomarkers might be much lower than the current reference standards. These findings can assist in better management of COVID-19 patients to improve outcomes. Importantly, the newly established cutoff levels of COVID-19-associated cardiac biomarkers may serve as useful criteria for the future prospective studies and clinical trials.
| S-EPMC7375179 | biostudies-literature
Project description:Aim of the studyMost survivors of an in-hospital cardiac arrest do not leave the hospital alive, and there is a need for a more patient-centered, holistic approach to the assessment of prognosis after an arrest. We sought to identify pre-, peri-, and post-arrest variables associated with in-hospital mortality amongst survivors of an in-hospital cardiac arrest.MethodsThis was a retrospective cohort study of patients ≥18 years of age who were resuscitated from an in-hospital arrest at our University Medical Center from January 1, 2013 to September 31, 2016. In-hospital mortality was chosen as a primary outcome and unfavorable discharge disposition (discharge disposition other than home or skilled nursing facility) as a secondary outcome.Results925 patients comprised the in-hospital arrest cohort with 305 patients failing to survive the arrest and a further 349 patients surviving the initial arrest but dying prior to hospital discharge, resulting in an overall survival of 29%. 620 patients with a ROSC of greater than 20 min following the in-hospital arrest were included in the final analysis. In a stepwise multivariable regression analysis, recurrent cardiac arrest, increasing age, time to ROSC, higher serum creatinine levels, and a history of cancer were predictors of in-hospital mortality. A history of hypertension was found to exert a protective effect on outcomes. In the regression model including serum lactate, increasing lactate levels were associated with lower odds of survival.ConclusionAmongst survivors of in-hospital cardiac arrest, recurrent cardiac arrest was the strongest predictor of poor outcomes with age, time to ROSC, pre-existing malignancy, and serum creatinine levels linked with increased odds of in-hospital mortality.
| S-EPMC8244474 | biostudies-literature
Project description:Multiple Biomarkers have recently been shown to be elevated in COVID-19, a respiratory infection with multi-organ dysfunction; however, information regarding the prognostic value of cardiac biomarkers as it relates to disease severity and cardiac injury are inconsistent. The goal of this meta-analysis was to summarize the evidence regarding the prognostic relevance of cardiac biomarkers from data available in published reports. PubMed, Embase and Web of Science were searched from inception through April 2020 for studies comparing median values of cardiac biomarkers in critically ill versus non-critically ill COVID-19 patients, or patients who died versus those who survived. The weighted mean differences (WMD) and 95% confidence interval (CI) between the groups were calculated for each study and combined using a random effects meta-analysis model. The odds ratio (OR) for mortality based on cardiac injury was combined from studies reporting it. Troponin levels were significantly higher in COVID-19 patients who died or were critically ill versus those who were alive or not critically ill (WMD 0.57, 95% CI 0.43-0.70, p < 0.001). Additionally, BNP levels were also significantly higher in patients who died or were critically ill (WMD 0.45, 95% CI - 0.21-0.69, p < 0.001). Cardiac injury was independently associated with significantly increased odds of mortality (OR 6.641, 95% CI 1.26-35.1, p = 0.03). A significant difference in levels of D-dimer was seen in those who died or were critically ill. CK levels were only significantly higher in those who died versus those who were alive (WMD 0.79, 95% CI 0.25-1.33, p = 0.004). Cardiac biomarkers add prognostic value to the determination of the severity of COVID-19 and can predict mortality.
| S-EPMC7925599 | biostudies-literature
Project description:Real-time automated continuous sampling of electronic medical record data may expeditiously identify patients at risk for death and enable prompt life-saving interventions. We hypothesized that a real-time electronic medical record-based alert could identify hospitalized patients at risk for mortality.An automated alert was developed and implemented to continuously sample electronic medical record data and trigger when at least 2 of 4 systemic inflammatory response syndrome criteria plus at least one of 14 acute organ dysfunction parameters was detected. The systemic inflammatory response syndrome and organ dysfunction alert was applied in real time to 312,214 patients in 24 hospitals and analyzed in 2 phases: training and validation datasets.In the training phase, 29,317 (18.8%) triggered the alert and 5.2% of such patients died, whereas only 0.2% without the alert died (unadjusted odds ratio 30.1; 95% confidence interval, 26.1-34.5; P < .0001). In the validation phase, the sensitivity, specificity, area under the curve, and positive and negative likelihood ratios for predicting mortality were 0.86, 0.82, 0.84, 4.9, and 0.16, respectively. Multivariate Cox-proportional hazard regression model revealed greater hospital mortality when the alert was triggered (adjusted hazards ratio 4.0; 95% confidence interval, 3.3-4.9; P < .0001). Triggering the alert was associated with additional hospitalization days (+3.0 days) and ventilator days (+1.6 days; P < .0001).An automated alert system that continuously samples electronic medical record data can be implemented, has excellent test characteristics, and can assist in the real-time identification of hospitalized patients at risk for death.
| S-EPMC4916370 | biostudies-literature
Project description:BackgroundTherapeutic hypothermia (TH) improves outcomes following cardiac arrest in small clinical trials.ObjectiveTo study real-world utilization and outcomes in US hospitals.DesignRetrospective cohort study.SettingCalifornia hospitals.PatientsPatients eligible for therapeutic hypothermia after cardiac arrest.InterventionsWe analyzed all discharges from California (1999-2008) to identify patients eligible for TH after cardiac arrest. Patients were considered eligible for TH if both cardiac arrest and anoxic brain injury were among the administrative diagnoses (n = 46,833). Patients undergoing TH (n = 204) were identified through billing codes.MeasurementsTH utilization and in-hospital mortality.ResultsUse of TH increased over the study period with 87.3% (178/204) of TH occurring between 2006 and 2008. Few hospitals appeared to perform TH over the study period (47/419, 11.2%). Utilization of TH was concentrated in a few centers, with the top 3 of 419 centers accounting for 31.4% (64/204) of cases. Patients undergoing TH were younger, less likely to be male, more likely to be treated at teaching centers, and had similar comorbidities compared to eligible individuals who did not undergo TH. The adjusted odds ratio for hospital mortality among patients undergoing TH was 0.80 (95% confidence interval [CI] 0.60-1.06, P = 0.11).ConclusionsTH utilization appears low, but implementation is increasing. Case selection and referral biases limit the analysis of the relationship between center TH volume and in-hospital mortality.
| S-EPMC3515738 | biostudies-literature
Project description:Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) leads to the outbreak of coronavirus disease 2019 (COVID-19), a worldwide epidemic disease affecting increasing number of patients. Although the virus primarily targets respiratory system, cardiovascular involvement has been reported in accumulating studies. In this review, we first describe the cardiac disorders in human with various types of CoV infection, and in animals infected with coronavirus. Particularly, we will focus on the association of cardiovascular disorders upon SARS-CoV-2 infection, and prognostic cardiac biomarkers in COVID-19. Besides, we will discuss the possible mechanisms underlying cardiac injury resulted from SARS-CoV-2 infection including direct myocardial injury caused by viral infection, reduced level of ACE2, and inflammatory response during infection. Improved understandings of cardiac disorders associated with COVID-19 might predict clinical outcome and provide insights into more rational treatment responses in clinical practice.
| S-EPMC7822398 | biostudies-literature
Project description:BackgroundProcesses for transferring patients to higher acuity facilities lack a standardized approach to prognostication, increasing the risk for low value care that imposes significant burdens on patients and their families with unclear benefits. We sought to develop a rapid and feasible tool for predicting mortality using variables readily available at the time of hospital transfer.Methods and findingsAll work was carried out at a single, large, multi-hospital integrated healthcare system. We used a retrospective cohort for model development consisting of patients aged 18 years or older transferred into the healthcare system from another hospital, hospice, skilled nursing or other healthcare facility with an admission priority of direct emergency admit. The cohort was randomly divided into training and test sets to develop first a 54-variable, and then a 14-variable gradient boosting model to predict the primary outcome of all cause in-hospital mortality. Secondary outcomes included 30-day and 90-day mortality and transition to comfort measures only or hospice care. For model validation, we used a prospective cohort consisting of all patients transferred to a single, tertiary care hospital from one of the 3 referring hospitals, excluding patients transferred for myocardial infarction or maternal labor and delivery. Prospective validation was performed by using a web-based tool to calculate the risk of mortality at the time of transfer. Observed outcomes were compared to predicted outcomes to assess model performance. The development cohort included 20,985 patients with 1,937 (9.2%) in-hospital mortalities, 2,884 (13.7%) 30-day mortalities, and 3,899 (18.6%) 90-day mortalities. The 14-variable gradient boosting model effectively predicted in-hospital, 30-day and 90-day mortality (c = 0.903 [95% CI:0.891-0.916]), c = 0.877 [95% CI:0.864-0.890]), and c = 0.869 [95% CI:0.857-0.881], respectively). The tool was proven feasible and valid for bedside implementation in a prospective cohort of 679 sequentially transferred patients for whom the bedside nurse calculated a SafeNET score at the time of transfer, taking only 4-5 minutes per patient with discrimination consistent with the development sample for in-hospital, 30-day and 90-day mortality (c = 0.836 [95%CI: 0.751-0.921], 0.815 [95% CI: 0.730-0.900], and 0.794 [95% CI: 0.725-0.864], respectively).ConclusionsThe SafeNET algorithm is feasible and valid for real-time, bedside mortality risk prediction at the time of hospital transfer. Work is ongoing to build pathways triggered by this score that direct needed resources to the patients at greatest risk of poor outcomes.
| S-EPMC7870086 | biostudies-literature
Project description:We profiled DNA from primary tumour RP tissue specimens from 15 patients (and adjacent normal tissue specimens from 4 patients) in a discovery cohort using whole-genome bisulphite sequencing (WGBS). We identified a set of differentially methylated regions in the primary tumour samples between patients who died ≤ 10 yrs post-RP and those still alive > 10 yrs post RP (median follow-up time: 19.5 years).
2022-10-03 | GSE158927 | GEO
Project description:BackgroundFor each of the COVID-19 pandemic waves, hospitals have had to plan for deploying surge capacity and resources to manage large but transient increases in COVID-19 admissions. While a lot of effort has gone into predicting regional trends in COVID-19 cases and hospitalizations, there are far fewer successful tools for creating accurate hospital-level forecasts.MethodsLarge-scale, anonymized mobile phone data has been shown to correlate with regional case counts during the first two waves of the pandemic (spring 2020, and fall/winter 2021). Building off this success, we developed a multi-step, recursive forecasting model to predict individual hospital admissions; this model incorporates the following data: (i) hospital-level COVID-19 admissions, (ii) statewide test positivity data, and (iii) aggregate measures of large-scale human mobility, contact patterns, and commuting volume.ResultsIncorporating large-scale, aggregate mobility data as exogenous variables in prediction models allows us to make hospital-specific COVID-19 admission forecasts 21 days ahead. We show this through highly accurate predictions of hospital admissions for five hospitals in Massachusetts during the first year of the COVID-19 pandemic.ConclusionsThe high predictive capability of the model was achieved by combining anonymized, aggregated mobile device data about users' contact patterns, commuting volume, and mobility range with COVID hospitalizations and test-positivity data. Mobility-informed forecasting models can increase the lead-time of accurate predictions for individual hospitals, giving managers valuable time to strategize how best to allocate resources to manage forthcoming surges.
| S-EPMC9927044 | biostudies-literature