Patients with prediabetes are at greater risk of developing diabetes 5 months postacute SARS-CoV-2 infection: a retrospective cohort study.

INTRODUCTION: Patients with prediabetes who contract SARS-CoV-2 infection (COVID-19) could be at higher risk of developing frank diabetes compared those who do not. This study aims to investigate the incidence of new-onset diabetes in patients with prediabetes after COVID-19 and if it differs from those not infected. RESEARCH DESIGN AND METHODS: Using electronic medical record data, 42 877 patients with COVID-19, 3102 were identified as having a history of prediabetes in the Montefiore Health System, Bronx, New York. During the same time period, 34 786 individuals without COVID-19 with history of prediabetes were identified and 9306 were propensity matched as controls. SARS-CoV-2 infection status was determined by a real-time PCR test between March 11, 2020 and August 17, 2022. The primary outcomes were new-onset in-hospital diabetes mellitus (I-DM) and new-onset persistent diabetes mellitus (P-DM) at 5 months after SARS-CoV-2 infection. RESULTS: Compared with hospitalized patients without COVID-19 with history of prediabetes, hospitalized patients with COVID-19 with history of prediabetes had a higher incidence of I-DM (21.9% vs 6.02%, p<0.001) and of P-DM 5 months postinfection (14.75% vs 7.51%, p<0.001). Non-hospitalized patients with and without COVID-19 with history of prediabetes had similar incidence of P-DM (4.15% and 4.1%, p>0.05). Critical illness (HR 4.6 (95% CI 3.5 to 6.1), p<0.005), in-hospital steroid treatment (HR 2.88 (95% CI 2.2 to 3.8), p<0.005), SARS-CoV-2 infection status (HR 1.8 (95% CI 1.4 to 2.3), p<0.005), and hemoglobin A1c (HbA1c) (HR 1.7 (95% CI 1.6 to 1.8), p<0.005) were significant predictors of I-DM. I-DM (HR 23.2 (95% CI 16.1 to 33.4), p<0.005), critical illness (HR 2.4 (95% CI 1.6 to 3.8), p<0.005), and HbA1c (HR 1.3 (95% CI 1.1 to 1.4), p<0.005) were significant predictors of P-DM at follow-up. CONCLUSIONS: SARS-CoV-2 infection confers a higher risk for developing persistent diabetes 5 months post-COVID-19 in patients with prediabetes who were hospitalized for COVID-19 compared with COVID-19-negative counterparts with prediabetes. In-hospital diabetes, critical illness, and elevated HbA1c are risk factors for developing persistent diabetes. Patients with prediabetes with severe COVID-19 disease may need more diligent monitoring for developing P-DM postacute SARS-CoV-2 infection.

Incidence, characteristics, risk factors and outcomes of diabetic ketoacidosis in COVID-19 patients: Comparison with influenza and pre-pandemic data.

AIMS: This study characterized incidence, patient profiles, risk factors and outcomes of in-hospital diabetic ketoacidosis (DKA) in patients with COVID-19 compared with influenza and pre-pandemic data. METHODS: This study consisted of 13 383 hospitalized patients with COVID-19 (March 2020-July 2022), 19 165 hospitalized patients with influenza (January 2018-July 2022) and 35 000 randomly sampled hospitalized pre-pandemic patients (January 2017-December 2019) in Montefiore Health System, Bronx, NY, USA. Primary outcomes were incidence of in-hospital DKA, in-hospital mortality, and insulin use at 3 and 6 months post-infection. Risk factors for developing DKA were identified. RESULTS: The overall incidence of DKA in patients with COVID-19 and influenza, and pre-pandemic were 2.1%, 1.4% and 0.5%, respectively (p < .05 pairwise). Patients with COVID-19 with DKA had worse acute outcomes (p < .05) and higher incidence of new insulin treatment 3 and 6 months post-infection compared with patients with influenza with DKA (p < .05). The incidence of DKA in patients with COVID-19 was highest among patients with type 1 diabetes (12.8%), followed by patients with insulin-dependent type 2 diabetes (T2D; 5.2%), non-insulin dependent T2D (2.3%) and, lastly, patients without T2D (1.3%). Patients with COVID-19 with DKA had worse disease severity and higher mortality [odds ratio = 6.178 (4.428-8.590), p < .0001] compared with those without DKA. Type 1 diabetes, steroid therapy for COVID-19, COVID-19 status, black race and male gender were associated with increased risk of DKA. CONCLUSIONS: The incidence of DKA was higher in COVID-19 cohort compared to the influenza and pre-pandemic cohort. Patients with COVID-19 with DKA had worse outcomes compared with those without. Many COVID-19 survivors who developed DKA during hospitalization became insulin dependent. Identification of risk factors for DKA and new insulin-dependency could enable careful monitoring and timely intervention.

Characteristics of COVID-19 patients with multiorgan injury across the pandemic in a large academic health system in the Bronx, New York.

Purpose: To investigate the evolution of COVID-19 patient characteristics and multiorgan injury across the pandemic. Methods: This retrospective cohort study consisted of 40,387 individuals tested positive for SARS-CoV-2 in the Montefiore Health System in Bronx, NY, between March 2020 and February 2022, of which 11,306 were hospitalized. Creatinine, troponin, and alanine aminotransferase were used to define acute kidney injury (AKI), acute cardiac injury (ACI) and acute liver injury, respectively. Demographics, comorbidities, emergency department visits, hospitalization, intensive care utilization, and mortality were analyzed across the pandemic. Results: COVID-19 positive cases, emergency department visits, hospitalization and mortality rate showed four distinct waves with a large first wave in April 2020, two small (Alpha and Delta) waves, and a large Omicron wave in December 2021. Omicron was more infectious but less lethal (p = 0.05). Among hospitalized COVID-19 patients, age decreased (p = 0.014), female percentage increased (p = 0.023), Hispanic (p = 0.028) and non-Hispanic Black (p = 0.05) percentages decreased, and patients with pre-existing diabetes (p = 0.002) and hypertension (p = 0.04) decreased across the pandemic. More than half (53.1%) of hospitalized patients had major organ injury. Patients with AKI, ACI and its combinations were older, more likely males, had more comorbidities, and consisted more of non-Hispanic Black and Hispanic patients (p = 0.005). Patients with AKI and its combinations had 4-9 times higher adjusted risk of mortality than those without. Conclusions: There were shifts in demographics toward younger age and proportionally more females with COVID-19 across the pandemic. While the overall trend showed improved clinical outcomes, a substantial number of COVID-19 patients developed multi-organ injuries over time. These findings could bring awareness to at-risk patients for long-term organ injuries and help to better inform public policy and outreach initiatives.

Longitudinal Chest X-ray Scores and their Relations with Clinical Variables and Outcomes in COVID-19 Patients.

Background: This study evaluated the temporal characteristics of lung chest X-ray (CXR) scores in COVID-19 patients during hospitalization and how they relate to other clinical variables and outcomes (alive or dead). Methods: This is a retrospective study of COVID-19 patients. CXR scores of disease severity were analyzed for: (i) survivors (N = 224) versus non-survivors (N = 28) in the general floor group, and (ii) survivors (N = 92) versus non-survivors (N = 56) in the invasive mechanical ventilation (IMV) group. Unpaired t-tests were used to compare survivors and non-survivors and between time points. Comparison across multiple time points used repeated measures ANOVA and corrected for multiple comparisons. Results: For general-floor patients, non-survivor CXR scores were significantly worse at admission compared to those of survivors (p < 0.05), and non-survivor CXR scores deteriorated at outcome (p < 0.05) whereas survivor CXR scores did not (p > 0.05). For IMV patients, survivor and non-survivor CXR scores were similar at intubation (p > 0.05), and both improved at outcome (p < 0.05), with survivor scores showing greater improvement (p < 0.05). Hospitalization and IMV duration were not different between groups (p > 0.05). CXR scores were significantly correlated with lactate dehydrogenase, respiratory rate, D-dimer, C-reactive protein, procalcitonin, ferritin, SpO2, and lymphocyte count (p < 0.05). Conclusions: Longitudinal CXR scores have the potential to provide prognosis, guide treatment, and monitor disease progression.

Incidence of new-onset in-hospital and persistent diabetes in COVID-19 patients: comparison with influenza.

BACKGROUND: This study investigated the incidences and risk factors associated with new-onset persistent type-2 diabetes during COVID-19 hospitalization and at 3-months follow-up compared to influenza. METHODS: This retrospective study consisted of 8216 hospitalized, 2998 non-hospitalized COVID-19 patients, and 2988 hospitalized influenza patients without history of pre-diabetes or diabetes in the Montefiore Health System in Bronx, New York. The primary outcomes were incidences of new-onset in-hospital type-2 diabetes mellitus (I-DM) and persistent diabetes mellitus (P-DM) at 3 months (average) follow-up. Predictive models used 80%/20% of data for training/testing with five-fold cross-validation. FINDINGS: I-DM was diagnosed in 22.6% of patients with COVID-19 compared to only 3.3% of patients with influenza (95% CI of difference [0.18, 0.20]). COVID-19 patients with I-DM compared to those without I-DM were older, more likely male, more likely to be treated with steroids and had more comorbidities. P-DM was diagnosed in 16.7% of hospitalized COVID-19 patients versus 12% of hospitalized influenza patients (95% CI of difference [0.03,0.065]) but only 7.3% of non-hospitalized COVID-19 patients (95% CI of difference [0.078,0.11]). The rates of P-DM significantly decreased from 23.9% to 4.0% over the studied period. Logistic regression identified similar risk factors predictive of P-DM for COVID-19 and influenza. The adjusted odds ratio (0.90 [95% CI 0.64,1.28]) for developing P-DM was not significantly different between the two viruses. INTERPRETATION: The incidence of new-onset type-2 diabetes was higher in patients with COVID-19 than influenza. Increased risk of diabetes associated with COVID-19 is mediated through disease severity, which plays a dominant role in the development of this post-acute infection sequela. FUNDING: None.

COVID-19 associated ketosis and diabetic ketoacidosis: A rapid review.

SARS-CoV-2 infection could disrupt the endocrine system directly or indirectly, which could result in endocrine dysfunction and glycaemic dysregulation, triggering transient or persistent diabetes mellitus. The literature on the complex relationship between COVID-19 and endocrine dysfunctions is still evolving and remains incompletely understood. Thus, we conducted a review on all literature to date involving COVID-19 associated ketosis or diabetic ketoacidosis (DKA). In total, 27 publications were included and analysed quantitatively and qualitatively. Studies included patients with DKA with existing or new onset diabetes. While the number of case and cohort studies was limited, DKA in the setting of COVID-19 seemed to increase risk of death, particularly in patients with new onset diabetes. Future studies with more specific variables and larger sample sizes are needed to draw better conclusions.

COVID-19 Patients in the COVID-19 Recovery and Engagement (CORE) Clinics in the Bronx.

Background: Early in the pandemic, we established COVID-19 Recovery and Engagement (CORE) Clinics in the Bronx and implemented a detailed evaluation protocol to assess physical, emotional, and cognitive function, pulmonary function tests, and imaging for COVID-19 survivors. Here, we report our findings up to five months post-acute COVID-19. Methods: Main outcomes and measures included pulmonary function tests, imaging tests, and a battery of symptom, physical, emotional, and cognitive assessments 5 months post-acute COVID-19. Findings: Dyspnea, fatigue, decreased exercise tolerance, brain fog, and shortness of breath were the most common symptoms but there were generally no significant differences between hospitalized and non-hospitalized cohorts (p > 0.05). Many patients had abnormal physical, emotional, and cognitive scores, but most functioned independently; there were no significant differences between hospitalized and non-hospitalized cohorts (p > 0.05). Six-minute walk tests, lung ultrasound, and diaphragm excursion were abnormal but only in the hospitalized cohort. Pulmonary function tests showed moderately restrictive pulmonary function only in the hospitalized cohort but no obstructive pulmonary function. Newly detected major neurological events, microvascular disease, atrophy, and white-matter changes were rare, but lung opacity and fibrosis-like findings were common after acute COVID-19. Interpretation: Many COVID-19 survivors experienced moderately restrictive pulmonary function, and significant symptoms across the physical, emotional, and cognitive health domains. Newly detected brain imaging abnormalities were rare, but lung imaging abnormalities were common. This study provides insights into post-acute sequelae following SARS-CoV-2 infection in neurological and pulmonary systems which may be used to support at-risk patients and develop effective screening methods and interventions.

Long-term outcomes of COVID-19 survivors with hospital AKI: association with time to recovery from AKI.

BACKGROUND: Although COVID-19 patients who developed in-hospital AKI have worse short-term outcomes, their long-term outcomes have not been fully characterized. We investigated 90-day and one-year outcomes after hospital AKI grouped by time to recovery from AKI. METHODS: This study consisted of 3,296 COVID-19 patients with hospital AKI stratified by early recovery (<48 hours), delayed recovery (2-7 days), and prolonged recovery (>7-90 days). Demographics, comorbidities, laboratory values were obtained at admission and up to one-year follow-up. Incidence of major adverse cardiovascular event (MACE) and major adverse kidney event (MAKE), rehospitalization, recurrent AKI, and new-onset chronic kidney disease (CKD) were obtained 90-days post COVID-19 discharge. RESULTS: The incidence of hospital AKI was 28.6%. Of COVID-19 patients with AKI, 58.0% experienced early recovery, 14.8% delayed recovery and 27.1% prolonged recovery. Patients with longer AKI recovery time had higher prevalence of CKD (p<0.05) and were more likely to need invasive mechanical ventilation (p<0.001) and to die (p<0.001). Many COVID-19 patients developed MAKE, recurrent AKI, and new-onset CKD within 90 days, and these incidences were higher in the prolonged recovery group (p<0.05). Incidence of MACE peaked 20-40 days post-discharge, whereas MAKE peaked 80-90 days post-discharge. Logistic regression models predicted 90-day MACE and MAKE with 82.4±1.6% and 79.6.9±2.3% accuracy, respectively. CONCLUSION: COVID-19 survivors who developed hospital AKI are at high risk for adverse cardiovascular and kidney outcomes, especially those with longer AKI recovery time and those with history of CKD. These patients may require long-term follow-up for cardiac and kidney complications.

Coronavirus Disease 2019 (COVID-19) Perinatal Outcomes Across the Pandemic at an Academic Medical Center in New York City.

OBJECTIVE: To investigate perinatal complications associated with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection during pregnancy in the four major waves of the coronavirus disease 2019 (COVID-19) pandemic in the Bronx, New York. METHODS: This retrospective cohort study included all patients who delivered at a single academic medical center between March 1, 2020, and February 13, 2022. SARS-CoV-2 positivity was defined as a positive SARS-CoV-2 test result during pregnancy. Primary outcomes were preterm birth, low birth weight, stillbirth, cesarean delivery, and preeclampsia associated with SARS-CoV-2 infection. Secondary analyses examined outcomes by predominant variant at the time of infection. Group differences in categorical variables were tested using χ2 tests. RESULTS: Of the 8,983 patients who delivered, 638 (7.1%) tested positive for SARS-CoV-2 infection during pregnancy. Age, race, ethnicity, and major comorbidities did not differ significantly between the SARS-CoV-2-positive and SARS-CoV-2-negative cohorts (P>.05). Primary outcomes did not differ between the SARS-CoV-2-positive and SARS-CoV-2-negative cohorts (P>.05). There was a marked increase in positive SARS-CoV-2 test results in individuals who gave birth during the Omicron wave (140/449, 31.2%). However, among patients who tested positive for SARS-CoV-2 infection, the preterm birth rate during the Omicron wave (9.9%) was significantly lower than during the original wave (20.3%) and the Alpha (18.4%) wave (P<.05). Vaccination rates were low before the Omicron wave and rose to 47.2% during the Omicron wave among individuals hospitalized with SARS-CoV-2 infection. Finally, second-trimester infection was significantly associated with worse perinatal outcomes compared with third-trimester infection (P<.05). CONCLUSION: There was a general trend toward improvement in preterm birth rates across the pandemic among pregnant patients with SARS-CoV-2 infection. The Omicron variant was more infectious, but the preterm birth rate during the Omicron wave was low compared with that during the original wave and the Alpha wave.

Longitudinal medical subspecialty follow-up of critically and non-critically ill hospitalized COVID-19 survivors up to 24 months after discharge.

Medical specialty usage of COVID-19 survivors after hospital discharge is poorly understood. This study investigated medical specialty usage at 1-12 and 13-24 months post-hospital discharge in critically ill and non-critically ill COVID-19 survivors. This retrospective study followed ICU (N = 89) and non-ICU (N = 205) COVID-19 survivors who returned for follow-up within the Stony Brook Health System post-hospital discharge. Follow-up data including survival, hospital readmission, ongoing symptoms, medical specialty care use, and ICU status were examined 1-12 and 13-24 months after COVID-19 discharge. "New" (not previously seen) medical specialty usage was also identified. Essentially all (98%) patients survived. Hospital readmission was 34%, but functional status scores at discharge were not associated with hospital readmission. Many patients reported ongoing [neuromuscular (50%) respiratory (39%), chronic fatigue (35%), cardiovascular (30%), gastrointestinal (28%), neurocognitive (22%), genitourinary (22%), and mood-related (13%)] symptoms at least once 1-24 months after discharge. Common specialty follow-ups included cardiology (25%), vascular medicine (17%), urology (17%), neurology (16%), and pulmonology (14%), with some associated with pre-existing comorbidities and with COVID-19. Common new specialty visits were vascular medicine (11%), pulmonology (11%), and neurology (9%). ICU patients had more symptoms and follow-ups compared to the non-ICU patients. This study reported high incidence of persistent symptoms and medical specialty care needs in hospitalized COVID-19 survivors 1-24 months post-discharge. Some specialty care needs were COVID-19 related or exacerbated by COVID-19 disease while others were associated with pre-existing medical conditions. Longer follow-up studies of COVID-19 survivor medical care needs are necessary.

Mammographic Findings of Diffuse Axillary Tail Trabecular Thickening After the Second Booster of COVID-19 Vaccination.

Axillary adenopathy post-coronavirus disease 2019 (COVID-19) vaccination has been well-documented and is seen with other types of vaccinations. Isolated trabecular thickening on mammography, however, is singular to COVID-19 vaccination, which implies that this finding may result from a distinct pathophysiologic mechanism. Herein, we describe the first case of axillary tail trabecular thickening resulting from the second booster of the COVID-19 vaccination series. Both breast cancer and mastitis may present similar findings. Ipsilateral injection of COVID-19 vaccine/booster and spontaneous resolution on follow-up provide clues to the etiology. It has been hypothesized that proinflammatory conditions may predispose to axillary tail trabecular thickening on mammography post-COVID-19 vaccination. Proinflammatory conditions such as hypertension, obesity, and diabetes may also predispose to breast cancer, making this scenario even more of a diagnostic dilemma. This scenario would more likely be seen in lower socioeconomic communities, African Americans, and Hispanics, who demonstrate a higher prevalence of these diseases, and who are also more vulnerable due to health care disparities negatively affecting these groups. We discuss our case and the importance of this public health issue. Sequela of COVID vaccination and boosters will be encountered in the foreseeable future and could pose a diagnostic dilemma, thus potentially straining the healthcare system with unnecessary biopsies and patient anxiety if not recognized and appropriately managed.

Clinical predictors of recovery of COVID-19 associated-abnormal liver function test 2 months after hospital discharge.

This study investigated whether acute liver injury (ALI) persisted and identified predictors of ALI recovery [as indicated by alanine aminotransferase (ALT) level] at hospital discharge and 2 months post-discharge for 7595 hospitalized COVID-19 patients from the Montefiore Health System (03/11/2020-06/03/2021). Mild liver injury (mLI) was defined as ALT = 1.5-5 ULN, and severe livery injury (sLI) was ALT ≥ 5 ULN. Logistic regression was used to identify predictors of ALI onset and recovery. There were 4571 (60.2%), 2306 (30.4%), 718 (9.5%) patients with no liver injury (nLI), mLI and sLI, respectively. Males showed higher incidence of sLI and mLI (p < 0.05). Mortality odds ratio was 4.15 [95% CI 3.41, 5.05, p < 0.001] for sLI and 1.69 [95% CI 1.47, 1.96, p < 0.001] for mLI compared to nLI. The top predictors (ALT, lactate dehydrogenase, ferritin, lymphocytes) accurately predicted sLI onset up to three days prior. Only 33.5% of mLI and 17.1% of sLI patients (survivors) recovered completely at hospital discharge. Most ALI patients (76.7-82.4%) recovered completely ~ 2 months post-discharge. The top predictors accurately predicted recovery post discharge with 83.2 ± 2.2% accuracy. In conclusion, most COVID-19 patients with ALI recovered completely ~ 2 months post discharge. Early identification of patients at-risk of persistent ALI could help to prevent long-term liver complications.

Deep learning of longitudinal chest X-ray and clinical variables predicts duration on ventilator and mortality in COVID-19 patients.

OBJECTIVES: To use deep learning of serial portable chest X-ray (pCXR) and clinical variables to predict mortality and duration on invasive mechanical ventilation (IMV) for Coronavirus disease 2019 (COVID-19) patients. METHODS: This is a retrospective study. Serial pCXR and serial clinical variables were analyzed for data from day 1, day 5, day 1-3, day 3-5, or day 1-5 on IMV (110 IMV survivors and 76 IMV non-survivors). The outcome variables were duration on IMV and mortality. With fivefold cross-validation, the performance of the proposed deep learning system was evaluated by receiver operating characteristic (ROC) analysis and correlation analysis. RESULTS: Predictive models using 5-consecutive-day data outperformed those using 3-consecutive-day and 1-day data. Prediction using data closer to the outcome was generally better (i.e., day 5 data performed better than day 1 data, and day 3-5 data performed better than day 1-3 data). Prediction performance was generally better for the combined pCXR and non-imaging clinical data than either alone. The combined pCXR and non-imaging data of 5 consecutive days predicted mortality with an accuracy of 85 ± 3.5% (95% confidence interval (CI)) and an area under the curve (AUC) of 0.87 ± 0.05 (95% CI) and predicted the duration needed to be on IMV to within 2.56 ± 0.21 (95% CI) days on the validation dataset. CONCLUSIONS: Deep learning of longitudinal pCXR and clinical data have the potential to accurately predict mortality and duration on IMV in COVID-19 patients. Longitudinal pCXR could have prognostic value if these findings can be validated in a large, multi-institutional cohort.

Long-short-term memory machine learning of longitudinal clinical data accurately predicts acute kidney injury onset in COVID-19: a two-center study.

OBJECTIVES: This study used the long-short-term memory (LSTM) artificial intelligence method to model multiple time points of clinical laboratory data, along with demographics and comorbidities, to predict hospital-acquired acute kidney injury (AKI) onset in patients with COVID-19. METHODS: Montefiore Health System data consisted of 1982 AKI and 2857 non-AKI (NAKI) hospitalized patients with COVID-19, and Stony Brook Hospital validation data consisted of 308 AKI and 721 NAKI hospitalized patients with COVID-19. Demographic, comorbidities, and longitudinal (3 days before AKI onset) laboratory tests were analyzed. LSTM was used to predict AKI with fivefold cross-validation (80%/20% for training/validation). RESULTS: The top predictors of AKI onset were glomerular filtration rate, lactate dehydrogenase, alanine aminotransferase, aspartate aminotransferase, and C-reactive protein. Longitudinal data yielded marked improvement in prediction accuracy over individual time points. The inclusion of comorbidities and demographics further improves prediction accuracy. The best model yielded an area under the curve, accuracy, sensitivity, and specificity to be 0.965 ± 0.003, 89.57 ± 1.64%, 0.95 ± 0.03, and 0.84 ± 0.05, respectively, for the Montefiore validation dataset, and 0.86 ± 0.01, 83.66 ± 2.53%, 0.66 ± 0.10, 0.89 ± 0.03, respectively, for the Stony Brook Hospital validation dataset. CONCLUSION: LSTM model of longitudinal clinical data accurately predicted AKI onset in patients with COVID-19. This approach could help heighten awareness of AKI complications and identify patients for early interventions to prevent long-term renal complications.

Deep-learning artificial intelligence analysis of clinical variables predicts mortality in COVID-19 patients.

Objective: The large number of clinical variables associated with coronavirus disease 2019 (COVID-19) infection makes it challenging for frontline physicians to effectively triage COVID-19 patients during the pandemic. This study aimed to develop an efficient deep-learning artificial intelligence algorithm to identify top clinical variable predictors and derive a risk stratification score system to help clinicians triage COVID-19 patients. Methods: This retrospective study consisted of 181 hospitalized patients with confirmed COVID-19 infection from January 29, 2020 to March 21, 2020 from a major hospital in Wuhan, China. The primary outcome was mortality. Demographics, comorbidities, vital signs, symptoms, and laboratory tests were collected at initial presentation, totaling 78 clinical variables. A deep-learning algorithm and a risk stratification score system were developed to predict mortality. Data were split into 85% training and 15% testing. Prediction performance was compared with those using COVID-19 severity score, CURB-65 score, and pneumonia severity index (PSI). Results: Of the 181 COVID-19 patients, 39 expired and 142 survived. Five top predictors of mortality were D-dimer, O2 Index, neutrophil:lymphocyte ratio, C-reactive protein, and lactate dehydrogenase. The top 5 predictors and the resultant risk score yielded, respectively, an area under curve (AUC) of 0.968 (95% CI = 0.87-1.0) and 0.954 (95% CI = 0.80-0.99) for the testing dataset. Our models outperformed COVID-19 severity score (AUC = 0.756), CURB-65 score (AUC = 0.671), and PSI (AUC = 0.838). The mortality rates for our risk stratification scores (0-5) were 0%, 0%, 6.7%, 18.2%, 67.7%, and 83.3%, respectively. Conclusions: Deep-learning prediction model and the resultant risk stratification score may prove useful in clinical decisionmaking under time-sensitive and resource-constrained environment.

New-onset and persistent neurological and psychiatric sequelae of COVID-19 compared to influenza: A retrospective cohort study in a large New York City healthcare network.

OBJECTIVES: Neurological and neuropsychiatric manifestations of post-acute SARS-CoV-2 infection (neuro-PASC) are common among COVID-19 survivors, but it is unknown how neuro-PASC differs from influenza-related neuro-sequelae. This study investigated the clinical characteristics of COVID-19 patients with and without new-onset neuro-PASC, and of flu patients with similar symptoms. METHODS: We retrospectively screened 18,811 COVID-19 patients and 5772 flu patients between January 2020 and June 2021 for the presence of new-onset neuro-sequelae that persisted at least 2 weeks past the date of COVID-19 or flu diagnosis. RESULTS: We observed 388 COVID-19 patients with neuro-PASC versus 149 flu patients with neuro-sequelae. Common neuro-PASC symptoms were anxiety (30%), depression (27%), dizziness (22%), altered mental status (17%), chronic headaches (17%), and nausea (11%). The average time to neuro-PASC onset was 138 days, with hospitalized patients reporting earlier onset than non-hospitalized patients. Neuro-PASC was associated with female sex and older age (p < 0.05), but not race, ethnicity, most comorbidities, or COVID-19 disease severity (p > 0.05). Compared to flu patients, COVID-19 patients were older, exhibited higher incidence of altered mental status, developed symptoms more quickly, and were prescribed psychiatric drugs more often (p < 0.05). CONCLUSIONS: This study provides additional insights into neuro-PASC risk factors and differentiates between post-COVID-19 and post-flu neuro-sequelae.

Outcomes of Hospitalized Patients With COVID-19 With Acute Kidney Injury and Acute Cardiac Injury.

PURPOSE: This study investigated the incidence, disease course, risk factors, and mortality in COVID-19 patients who developed both acute kidney injury (AKI) and acute cardiac injury (ACI), and compared to those with AKI only, ACI only, and no injury (NI). METHODS: This retrospective study consisted of hospitalized COVID-19 patients at Montefiore Health System in Bronx, New York between March 11, 2020 and January 29, 2021. Demographics, comorbidities, vitals, and laboratory tests were collected during hospitalization. Predictive models were used to predict AKI, ACI, and AKI-ACI onset. Longitudinal laboratory tests were analyzed with time-lock to discharge alive or death. RESULTS: Of the 5,896 hospitalized COVID-19 patients, 44, 19, 9, and 28% had NI, AKI, ACI, and AKI-ACI, respectively. Most ACI presented very early (within a day or two) during hospitalization in contrast to AKI (p < 0.05). Patients with combined AKI-ACI were significantly older, more often men and had more comorbidities, and higher levels of cardiac, kidney, liver, inflammatory, and immunological markers compared to those of the AKI, ACI, and NI groups. The adjusted hospital-mortality odds ratios were 17.1 [95% CI = 13.6-21.7, p < 0.001], 7.2 [95% CI = 5.4-9.6, p < 0.001], and 4.7 [95% CI = 3.7-6.1, p < 0.001] for AKI-ACI, ACI, and AKI, respectively, relative to NI. A predictive model of AKI-ACI onset using top predictors yielded 97% accuracy. Longitudinal laboratory data predicted mortality of AKI-ACI patients up to 5 days prior to outcome, with an area-under-the-curve, ranging from 0.68 to 0.89. CONCLUSIONS: COVID-19 patients with AKI-ACI had markedly worse outcomes compared to those only AKI, ACI and NI. Common laboratory variables accurately predicted AKI-ACI. The ability to identify patients at risk for AKI-ACI could lead to earlier intervention and improvement in clinical outcomes.

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.