An electrocardiography score predicts heart failure hospitalization or death beyond that of cardiovascular magnetic resonance imaging

Background: The electrocardiogram (ECG) and cardiovascular magnetic resonance imaging (CMR) both provide powerful prognostic information. The aim was to determine the relative prognostic value of ECG and CMR, respectively. Methods: Consecutive patients (n=783) undergoing CMR and resting 12-lead ECG with a QRS duration <120 ms were included. CMR measures included feature tracking global longitudinal strain (GLS), extracellular volume fraction (ECV), left ventricular mass and volumes, and ischemic and non-ischemic scar size. Prognosis scores for one-year event-free survival were derived using continuous ECG or CMR measures, and multinomial logistic regression, and compared with regards to the combined outcome of survival free from hospitalization for heart failure or death. Results: Patients (median [interquartile range] age 55 [43-64] years, 44% female) had 155 events during 5.7 [4.4-6.6] years. The ECG prognosis score included 1) the frontal plane QRS-T angle, and 2) the heart rate corrected QT duration (QTc) (log-rank 55, p<0.001). The CMR prognosis score included 1) GLS, and 2) ECV (log-rank 85, p<0.001). The combination of positive scores for both ECG and CMR yielded the highest prognostic value (log-rank 105, p<0.001). Multivariable analysis showed an association with outcomes for both the ECG prognosis score (log-rank 8.4, hazard ratio [95% confidence interval] 1.29 [1.09-1.54], p=0.004) and the CMR prognosis score (log-rank 47, hazard ratio 1.90 [1.58-2.28], p<0.001). Conclusions: An ECG prognosis score predicted outcomes independently of, and beyond CMR. Combining the results of ECG and CMR using both prognosis scores improved the overall prognostic performance.

A Review of Prognosis Model Associated With Cardiogenic Shock After Acute Myocardial Infarction

Objective: Cardiogenic shock seriously affects the survival rate of patients. However, few prognostic models are concerned with the score of cardiogenic shock, and few clinical studies have validated it. In order to optimize the diagnosis and treatment of myocardial infarction complicated with cardiogenic shock and facilitate the classification of clinical trials, the prognosis score model is urgently needed.Methods: Cardiogenic shock, severe case, prognosis score, myocardial infarction and external verification were used as the search terms to search PubMed, Embase, Web of Science, Cochrane, EBSCO (Medline), Scopus, BMC, NCBI, Oxford Academy, Science Direct, and other databases for pertinent studies published up until 1 August 2021. There are no restrictions on publication status and start date. Filter headlines and abstracts to find articles that may be relevant. The list of references for major studies was reviewed to obtain more references.Results and Conclusions: The existing related models are in urgent need of more external clinical verifications. In the meanwhile, with the development of molecular omics and the clinical need for optimal treatment of CS, it is urgent to establish a prognosis model with higher differentiation and coincidence rates.

Mechanical Ventilation and Death During Pregnancy Complicated by COVID-19: A Prognostic Analysis from the Brazilian COVID-19 Registry Score

Background: Assessing predictors of critical outcomes in COVID-19 may advise timely treatments and better prepare facilities to overcome extra adversities during pregnancy. However, many clinical parameters of existent scores are deeply modified by physiologic adaptations. Our aim was to assess the feasibility of a prognosis score developed for general hospitalized adults with COVID-19 in Brazil to predict clinical adverse outcomes in pregnant women upon hospital admission.Methods: This is a multicenter retrospective substudy of the Brazilian COVID-19 Registry, a multicenter cohort analysis in Brazilian hospitals, which provided an accurate score to predict in-hospital death. The present analysis assessed the performance of this model, ABC2-SPH, based on data of 3978 patients, to assess poor clinical outcomes in data from 85 pregnant women admitted due to COVID-19 from March 1, 2020, to May 5, 2021, in 19 Brazilian hospitals. The primary outcomes were death and the composite mechanical ventilation or death, and secondary were pregnancy outcomes and severe/critical Covid-19. The overall discrimination of the model was presented as the area under the receiver operating characteristic curve (AUROC).Results: Thirty-one (36.5%) pregnant women had critical or severe COVID-19. Most of them had no previous comorbidities (64.7%). The median gestational age was 31.0 (26.0, 36.2) weeks; 38 (44.7%) women gave birth during hospitalization by Covid-19, most of them by C-section (76.3%). The need for mechanical ventilation or death occurred in 14 (17.3%) pregnant women. Severe and critical COVID-19 in pregnant women was associated with diabetes, inflammatory markers, and abnormal vital signals observed at admission. The model was not able to identify adverse clinical outcomes. The AUROC of predicting severe/critical Covid-19 illness was 0.595 (95% CI: 0.424-0.754); AUROC of the inpatient death discrimination was 0.683 (95% CI: 0.293-0.945), as the AUROC of mechanical ventilation or death discrimination was 0.591 (95% CI: 0.434-0.75).Conclusions: The model ABC2-SPH developed in Brazilian general patients was not able to identify adverse clinical outcomes in pregnant women with COVID-19. We warn against the use of general inpatients COVID-19 prognosis in pregnant women. A more useful model for clinical prognosis is necessary concerning the specificities of pregnancy affected by COVID-19.

Identifying early-phase clinical trial (EP-CT) participants at risk for poor outcomes.

301 Background: EP-CTs investigate novel treatment options, with recent advances in personalized therapy leading to increased response rates, decreased toxicity, and improved survival. Identifying EP-CT participants at risk for poor outcomes could help identify those who may benefit most from targeted supportive care efforts. Methods: We retrospectively reviewed the electronic health records of consecutive patients enrolled in EP-CTs from 2017-2019 to obtain baseline characteristics (demographics and clinical factors), clinical outcomes (survival, time on trial, completion of dose-limiting toxicity [DLT] period, emergency room [ER] visits, hospitalizations, and hospice use), and receipt of supportive care services before/during trial (palliative care, social work, physical therapy [PT], and nutrition). We calculated the validated Royal Marsden Hospital (RMH) prognosis score using data at the time of EP-CT enrollment based on patients’ lactate dehydrogenase, serum albumin, and number of sites of metastasis. RMH scores range from 0-3, with scores of 2+ indicating a poor prognosis. We examined differences in patient characteristics, clinical outcomes, and receipt of supportive care services based on the RMH prognosis score. Results: Among 350 patients (median age = 63.2 years [range 23.0-84.3]; 57.1% female, 98.0% metastatic cancer), the most common cancer types were lung (23.4%), gastrointestinal (20.3%), and breast (12.0%). Nearly one-third (31.7%) had an RMH score indicating a poor prognosis. Patients with a poor prognosis RMH score had a worse performance status (ECOG ≥1: 80.2% vs 58.1%, p <.001) and more prior treatment (3+ prior lines: 48.6% vs 34.7%, p =.001) than those with a better prognosis score. Those with a poor prognosis RMH score had worse survival (median: 147 vs 402 days, p <.001) and shorter time on trial (median: 49 vs 84 days, HR = 1.53, p <.001), as well as a lower likelihood of completing the DLT period (72.1% vs 80.8%, p =.015). Patients with a poor prognosis score had a higher risk for ER visits (HR 1.66; p =.037) and hospitalizations (HR 1.69; p =.016) while on trial, with earlier hospice enrollment (HR 2.22; p =.006) following the trial. Patients with a poor prognosis score were significantly more likely to receive palliative care before/during trial (46.8% vs 27.6% p =.001), but not social work (41.4% vs 41.4% p = 1.00), PT (44.1% vs 34.7%; p =.098), or nutrition (40.5% vs 37.2%; p =.557). Conclusions: EP-CT participants represent a unique population of patients with advanced cancer, and we identified a group at risk for particularly poor outcomes, including worse survival, shorter time on trial, and greater use of healthcare services. Although patients with a poor prognosis score had higher rates of palliative care use, under half received supportive care services, underscoring the need for efforts to prospectively target these patients with interventions that address their supportive care needs.

Prognosis Score System to Predict Survival for COVID-19 Cases: a Korean Nationwide Cohort Study

Background As the COVID-19 pandemic continues, an initial risk-adapted allocation is crucial for managing medical resources and providing intensive care. Objective In this study, we aimed to identify factors that predict the overall survival rate for COVID-19 cases and develop a COVID-19 prognosis score (COPS) system based on these factors. In addition, disease severity and the length of hospital stay for patients with COVID-19 were analyzed. Methods We retrospectively analyzed a nationwide cohort of laboratory-confirmed COVID-19 cases between January and April 2020 in Korea. The cohort was split randomly into a development cohort and a validation cohort with a 2:1 ratio. In the development cohort (n=3729), we tried to identify factors associated with overall survival and develop a scoring system to predict the overall survival rate by using parameters identified by the Cox proportional hazard regression model with bootstrapping methods. In the validation cohort (n=1865), we evaluated the prediction accuracy using the area under the receiver operating characteristic curve. The score of each variable in the COPS system was rounded off following the log-scaled conversion of the adjusted hazard ratio. Results Among the 5594 patients included in this analysis, 234 (4.2%) died after receiving a COVID-19 diagnosis. In the development cohort, six parameters were significantly related to poor overall survival: older age, dementia, chronic renal failure, dyspnea, mental disturbance, and absolute lymphocyte count <1000/mm3. The following risk groups were formed: low-risk (score 0-2), intermediate-risk (score 3), high-risk (score 4), and very high-risk (score 5-7) groups. The COPS system yielded an area under the curve value of 0.918 for predicting the 14-day survival rate and 0.896 for predicting the 28-day survival rate in the validation cohort. Using the COPS system, 28-day survival rates were discriminatively estimated at 99.8%, 95.4%, 82.3%, and 55.1% in the low-risk, intermediate-risk, high-risk, and very high-risk groups, respectively, of the total cohort (P<.001). The length of hospital stay and disease severity were directly associated with overall survival (P<.001), and the hospital stay duration was significantly longer among survivors (mean 26.1, SD 10.7 days) than among nonsurvivors (mean 15.6, SD 13.3 days). Conclusions The newly developed predictive COPS system may assist in making risk-adapted decisions for the allocation of medical resources, including intensive care, during the COVID-19 pandemic.