Clinical Attributes and Risk Factors for In- Hospital Mortality among Covid-19 Patients in a Community Hospital Setting: A Propensity Matched Analysis

Background. New York City emerged as the Epicenter for Covid-19 due to novel Coronavirus SARS-CoV-2 soon after it was declared a Global Pandemic in early 2020 by the WHO. Covid-19 presents with a wide spectrum of illness from asymptomatic to severe respiratory failure, shock, multiorgan failure and death. Although the overall fatality rate is low, there is significant mortality among hospitalized patients. There is limited information exploring the impact of Covid-19 in community hospital settings in ethnically diverse populations. We aimed to identify risk factors for Covid-19 mortality in our institution. Methods. We conducted a retrospective cohort study of hospitalized in our institution for Covid 19 from March 1st to June 21st 2020. It comprised of 425 discharged patients and 245 expired patients. Information was extracted from our EMR which included demographics, presenting symptoms, and laboratory data. We propensity matched 245 expired patients with a concurrent cohort of discharged patients. Statistically significant covariates were applied in matching, which included age, gender, race, body mass index (BMI), diabetes mellitus, and hypertension. The admission clinical attributes and laboratory parameters and outcomes were analyzed. Results. The mean age of the matched cohort was 66.9 years. Expired patients had a higher incidence of dyspnea (P < 0.001) and headache (0.031). In addition, expired patients had elevated CRP- hs (mg/dl) ≥ 123 (< .0001), SGOT or AST (IU/L) ≥ 54 (p < 0.001), SGPT or ALT (IU/L) ≥ 41 (p < 0.001), and creatinine (mg/dl) ≥ 1.135 (0.001), lower WBC counts (k/ul) ≥ 8.42 (0.009). Furthermore, on multivariate logistic regression, dyspnea (OR = 2.56, P < 0.001), creatinine ≥ 1.135 (OR = 1.79, P = 0.007), LDH(U/L) > 465 (OR = 2.18, P = 0.001), systolic blood pressure < 90 mm Hg (OR = 4.28, p = .02), respiratory rate > 24 (OR = 2.88, p = .001), absolute lymphocyte percent (≤ 12%) (OR = 1.68, p = .001) and procalcitonin (ng/ml) ≥ 0.305 (OR = 1.71, P = .027) predicted in- hospital mortality in all matched patients. Conclusion. Our case series provides admission clinical characteristics and laboratory parameters that predict in- hospital mortality in propensity Covid 19 matched patients with a large Hispanic population. These risk factors will require further validation.

Significant Decrease in the Incidence Rate of Common Outpatient Upper Respiratory Tract Infection Diagnoses per Clinic Visit in the First Respiratory Season of October 2020 to March 2021 during the Covid-19 Pandemic. A Report from an Outpatient Antimicrobial Stewardship Program at a community hospital in Brooklyn

Background. As part of our outpatient Antimicrobial Stewardship Program, we do surveillance of diagnoses and antibiotic use for common upper respiratory tract infections such as acute upper respiratory tract infection, acute bronchitis, sinusitis, and pharyngitis. We sought to evaluate the impact of the Covid-19 pandemic on the incidence rate of upper respiratory tract infection diagnoses per clinic visit during October 2020 to March 2021 season compared to the three prior respiratory seasons. We also sought to reflect of impact of increase in televisits and overlapping symptoms of COVID 19 and upper respiratory tract infections. Methods. Our cohort study extending from October 2017 to March 2021. We collected number of diagnoses of upper respiratory infections and number of unique clinic visits during four consecutive respiratory seasons at our primary care sites via electronic health records. Results. During the recent October 2020 to March 2021 respiratory season which coincided with the second NYC Covid-19 wave, we had 11569 unique clinic visits and 39 diagnoses of an upper respiratory tract infection - incident rate of 1.29. In the three prior respiratory seasons combined, we had 40939 unique clinic visits and 833 diagnoses of an upper respiratory tract infection - incident rate of 1.49. The incident rates showed a dramatic decline using the test based method and the chi square-statistic p< 0.0001 with an incident rate ratio using a poisson exact method of 6.0359. Statistical comparisons of the current season to each prior individual season yielded similar results. The percentage of Tele-visits during the current season was 19% compared to 0% in the 3 prior seasons. Conclusion. During the first respiratory season from October 2020 to March 2021 in midst of the Covid-19 pandemic which also coincided with the second Covid-19 wave in New York, we saw a statistically significant decrease in incidence of common upper respiratory tract infection diagnoses per clinic visit compared to the three prior respiratory seasons. Overlapping signs and symptoms of upper respiratory tract infections and Covid-19 with the added percentage in Tele-visits did not cause an increase in incidence rates of upper respiratory tract infection diagnoses. Covid-19 related mitigation efforts may have played a role.

CLINICAL ATTRIBUTES AND RISK FACTORS FOR MECHANICAL VENTILATION AMONG PATIENTS WITH COVID-19 IN A COMMUNITY HOSPITAL SETTING: A PROPENSITY-MATCHED ANALYSIS

TOPIC: Chest Infections TYPE: Original Investigations PURPOSE: Covid-19 caused by the novel SARS-CoV-2 has emerged as a global health crisis with various clinical complications. Covid-19 related respiratory manifestations have been reported as mild, moderate to severe including acute lung injury and acute respiratory distress syndrome necessitating non-invasive forms of oxygenation to mechanical ventilation (MV). MV patients frequently undergo prolonged hospitalizations with substantial morbidity and mortality. We sought to evaluate risk factors for MV in our cohort. METHODS: We conducted a retrospective cohort study of patients admitted in our institution from March 1st to June 21st2020, to assess risk factors for Covid-19 related respiratory failure requiring MV. The original cohort encompassed 166 MV and 503 non MV patients. Information from our hospital medical records was extracted, which included demographics, presenting symptoms, past medical history, vital signals, and laboratory data and need for MV. We propensity matched 166 MV with a concurrent cohort of non MV patients in our institution. Covariates applied in matching included age, gender, race, and body mass index (BMI). The admission clinical attributes and laboratory parameters were analyzed, along with outcomes. RESULTS: The mean age of our matched cohort was 63.8 years. Mechanically Ventilated patients had a higher incidence of tachycardia (heart rate > 125) (p <.001), elevated respiratory rate > 24 cycles per minute (p <.001), fever > 97.8 F (Temperature > (p =.037), shortness of breath (p =.001), and headaches (p =.005). In addition, mechanically ventilated patients had a lower serum albumin (g/dl) ≤ 3 units (p <. 001), elevated serum creatinine (mg/dl) ≥ 1.135 units (p =.02), elevated serum CRP-HS ≥ 123 units (p =.005), HbA1C (%) > 6.6 units (p =.004), serum lactic acid (mmol/L) > 1.7 units (p =.003), serum LDH U/L > 465 U/L (p <.001), Procalcitonin (ng/ml) >.305 units (p <0.001), SGOT IU/L or AST IU/L ≥ 54 units (p < 0.001), SGPT or ALT IU/L ≥ 41 units (p =.021), and WBC count > 8.4 k/ul (p <.001). Furthermore, tachycardia (OR = 3.98, p =.001), HbA1C (OR = 2.36, p =.008), serum LDH (OR = 1.9, p =.041), and absolute lymphocyte percent ≤ 12 (OR = 1.98, p =.022) predicted mechanical ventilation in all matched patients in our institutional cohort. CONCLUSIONS: Our case series provides clinical characteristics, laboratory parameters, and predictors for mechanical ventilation in matched patients with Covid-19. Elevated heart rate, HbA1C, serum LDH and decreased lymphocyte percentage were predictors for mechanical ventilation. Tachycardia had the highest odds of 3.98. CLINICAL IMPLICATIONS: Several clinical and laboratory parameters can be utilized for evaluating and stratifying Covid-19 patients’ risk for mechanical ventilation. These risk factors will need further validation in other similar cohorts. DISCLOSURES: No relevant relationships by Olawale Akande, source=Web Response No relevant relationships by Olga Badem, source=Web Response No relevant relationships by Premila Bhat, source=Web Response No relevant relationships by Utpal Bhatt, source=Web Response No relevant relationships by Diego Castellon, source=Web Response No relevant relationships by Bhargav Desai, source=Web Response No relevant relationships by Basilides Fermin, source=Web Response No relevant relationships by Shurovi Jafar, source=Web Response No relevant relationships by KELASH KUMAR, source=Web Response No relevant relationships by Juan Martinez Zegarra, source=Web Response No relevant relationships by Tanveer Mir, source=Web Response No relevant relationships by Parvez Mir, source=Web Response No relevant relationships by Luis Morón Mercado, source=Web Response No relevant relationships by Beatriz Omeragic, source=Web Response No relevant relationships by Maxine Orris, source=Web Response No relevant relationships by Priyank Patel, source=Web Response No relevant relationships by Giovanna Ramirez-Barbieri, source=Web Response No relevant relationships by Luis Santana Alcantara, source=Web Response No relevant relationships by Karthik Seetharam, source=Web Response No relevant relationships by Jilan Shah, source=Web Response No relevant relationships by Phanthira Tamsukhin, source=Web Response No relevant relationships by Zeyar Thet, source=Web Response No relevant relationships by Elbia Toribio, source=Web Response No relevant relationships by Thinzar Wai, source=Web Response No relevant relationships by Vamsi Yenugadhati, source=Web Response