Clinical predictors of acute cardiac injury and normalization of troponin after hospital discharge from COVID-19.

BACKGROUND: Although acute cardiac injury (ACI) is a known COVID-19 complication, whether ACI acquired during COVID-19 recovers is unknown. This study investigated the incidence of persistent ACI and identified clinical predictors of ACI recovery in hospitalized patients with COVID-19 2.5 months post-discharge. METHODS: This retrospective study consisted of 10,696 hospitalized COVID-19 patients from March 11, 2020 to June 3, 2021. Demographics, comorbidities, and laboratory tests were collected at ACI onset, hospital discharge, and 2.5 months post-discharge. ACI was defined as serum troponin-T (TNT) level >99th-percentile upper reference limit (0.014ng/mL) during hospitalization, and recovery was defined as TNT below this threshold 2.5 months post-discharge. Four models were used to predict ACI recovery status. RESULTS: There were 4,248 (39.7%) COVID-19 patients with ACI, with most (93%) developed ACI on or within a day after admission. In-hospital mortality odds ratio of ACI patients was 4.45 [95%CI: 3.92, 5.05, p<0.001] compared to non-ACI patients. Of the 2,880 ACI survivors, 1,114 (38.7%) returned to our hospitals 2.5 months on average post-discharge, of which only 302 (44.9%) out of 673 patients recovered from ACI. There were no significant differences in demographics, race, ethnicity, major commodities, and length of hospital stay between groups. Prediction of ACI recovery post-discharge using the top predictors (troponin, creatinine, lymphocyte, sodium, lactate dehydrogenase, lymphocytes and hematocrit) at discharge yielded 63.73%-75.73% accuracy. INTERPRETATION: Persistent cardiac injury is common among COVID-19 survivors. Readily available patient data accurately predict ACI recovery post-discharge. Early identification of at-risk patients could help prevent long-term cardiovascular complications. FUNDING: None.

Prophylactic versus therapeutic dose anticoagulation effects on survival among critically ill patients with COVID-19.

INTRODUCTION: Although patients with severe COVID-19 are known to be at high risk of developing thrombotic events, the effects of anticoagulation (AC) dose and duration on in-hospital mortality in critically ill patients remain poorly understood and controversial. The goal of this study was to investigate survival of critically ill COVID-19 patients who received prophylactic or therapeutic dose AC and analyze the mortality rate with respect to detailed demographic and clinical characteristics. MATERIALS AND METHODS: We conducted a retrospective, observational study of critically ill COVID-19 patients admitted to the ICU at Stony Brook University Hospital in New York who received either prophylactic (n = 158) or therapeutic dose AC (n = 153). Primary outcome was in-hospital death assessed by survival analysis and covariate-adjusted Cox proportional hazard model. RESULTS: For the first 3 weeks of ICU stay, we observed similar survival curves for prophylactic and therapeutic AC groups. However, after 3 or more weeks of ICU stay, the therapeutic AC group, characterized by high incidence of acute kidney injury (AKI), had markedly higher death incidence rates with 8.6 deaths (95% CI = 6.2-11.9 deaths) per 1,000 person-days and about 5 times higher risk of death (adj. HR = 4.89, 95% CI = 1.71-14.0, p = 0.003) than the prophylactic group (2.4 deaths [95% CI = 0.9-6.3 deaths] per 1,000 person-days). Among therapeutic AC users with prolonged ICU admission, non-survivors were characterized by older males with depressed lymphocyte counts and cardiovascular disease. CONCLUSIONS: Our findings raise the possibility that prolonged use of high dose AC, independent of thrombotic events or clinical background, might be associated with higher risk of in-hospital mortality. Moreover, AKI, age, lymphocyte count, and cardiovascular disease may represent important risk factors that could help identify at-risk patients who require long-term hospitalization with therapeutic dose AC treatment.

Neural network analysis of clinical variables predicts escalated care in COVID-19 patients: a retrospective study.

This study sought to identify the most important clinical variables that can be used to determine which COVID-19 patients hospitalized in the general floor will need escalated care early on using neural networks (NNs). Analysis was performed on hospitalized COVID-19 patients between 7 February 2020 and 4 May 2020 in Stony Brook Hospital. Demographics, comorbidities, laboratory tests, vital signs and blood gases were collected. We compared those data obtained at the time in emergency department and the time of intensive care unit (ICU) upgrade of: (i) COVID-19 patients admitted to the general floor (N = 1203) vs. those directly admitted to ICU (N = 104), and (ii) patients not upgraded to ICU (N = 979) vs. those upgraded to the ICU (N = 224) from the general floor. A NN algorithm was used to predict ICU admission, with 80% training and 20% testing. Prediction performance used area under the curve (AUC) of the receiver operating characteristic analysis (ROC). We found that C-reactive protein, lactate dehydrogenase, creatinine, white-blood cell count, D-dimer and lymphocyte count showed temporal divergence between COVID-19 patients hospitalized in the general floor that were upgraded to ICU compared to those that were not. The NN predictive model essentially ranked the same laboratory variables to be important predictors of needing ICU care. The AUC for predicting ICU admission was 0.782 ± 0.013 for the test dataset. Adding vital sign and blood-gas data improved AUC (0.822 ± 0.018). This work could help frontline physicians to anticipate downstream ICU need to more effectively allocate healthcare resources.