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2.
PLoS One ; 15(9): e0239536, 2020.
Article in English | MEDLINE | ID: covidwho-807661

ABSTRACT

BACKGROUND: The 2019 novel coronavirus disease (COVID-19) has created unprecedented medical challenges. There remains a need for validated risk prediction models to assess short-term mortality risk among hospitalized patients with COVID-19. The objective of this study was to develop and validate a 7-day and 14-day mortality risk prediction model for patients hospitalized with COVID-19. METHODS: We performed a multicenter retrospective cohort study with a separate multicenter cohort for external validation using two hospitals in New York, NY, and 9 hospitals in Massachusetts, respectively. A total of 664 patients in NY and 265 patients with COVID-19 in Massachusetts, hospitalized from March to April 2020. RESULTS: We developed a risk model consisting of patient age, hypoxia severity, mean arterial pressure and presence of kidney dysfunction at hospital presentation. Multivariable regression model was based on risk factors selected from univariable and Chi-squared automatic interaction detection analyses. Validation was by receiver operating characteristic curve (discrimination) and Hosmer-Lemeshow goodness of fit (GOF) test (calibration). In internal cross-validation, prediction of 7-day mortality had an AUC of 0.86 (95%CI 0.74-0.98; GOF p = 0.744); while 14-day had an AUC of 0.83 (95%CI 0.69-0.97; GOF p = 0.588). External validation was achieved using 265 patients from an outside cohort and confirmed 7- and 14-day mortality prediction performance with an AUC of 0.85 (95%CI 0.78-0.92; GOF p = 0.340) and 0.83 (95%CI 0.76-0.89; GOF p = 0.471) respectively, along with excellent calibration. Retrospective data collection, short follow-up time, and development in COVID-19 epicenter may limit model generalizability. CONCLUSIONS: The COVID-AID risk tool is a well-calibrated model that demonstrates accuracy in the prediction of both 7-day and 14-day mortality risk among patients hospitalized with COVID-19. This prediction score could assist with resource utilization, patient and caregiver education, and provide a risk stratification instrument for future research trials.


Subject(s)
Coronavirus Infections/mortality , Logistic Models , Pneumonia, Viral/mortality , Risk Assessment/methods , Aged , Aged, 80 and over , Betacoronavirus , Female , Hospital Mortality , Hospitalization , Humans , Male , Massachusetts , Middle Aged , New York , Pandemics , ROC Curve , Regression Analysis , Retrospective Studies , Risk Factors , United States
3.
J Med Internet Res ; 22(9): e23565, 2020 09 25.
Article in English | MEDLINE | ID: covidwho-801719

ABSTRACT

BACKGROUND: Northwell Health, an integrated health system in New York, has treated more than 15,000 inpatients with COVID-19 at the US epicenter of the SARS-CoV-2 pandemic. OBJECTIVE: We describe the demographic characteristics of patients who died of COVID-19, observation of frequent rapid response team/cardiac arrest (RRT/CA) calls for non-intensive care unit (ICU) patients, and factors that contributed to RRT/CA calls. METHODS: A team of registered nurses reviewed the medical records of inpatients who tested positive for SARS-CoV-2 via polymerase chain reaction before or on admission and who died between March 13 (first Northwell Health inpatient expiration) and April 30, 2020, at 15 Northwell Health hospitals. The findings for these patients were abstracted into a database and statistically analyzed. RESULTS: Of 2634 patients who died of COVID-19, 1478 (56.1%) had oxygen saturation levels ≥90% on presentation and required no respiratory support. At least one RRT/CA was called on 1112/2634 patients (42.2%) at a non-ICU level of care. Before the RRT/CA call, the most recent oxygen saturation levels for 852/1112 (76.6%) of these non-ICU patients were at least 90%. At the time the RRT/CA was called, 479/1112 patients (43.1%) had an oxygen saturation of <80%. CONCLUSIONS: This study represents one of the largest reviewed cohorts of mortality that also captures data in nonstructured fields. Approximately 50% of deaths occurred at a non-ICU level of care despite admission to the appropriate care setting with normal staffing. The data imply a sudden, unexpected deterioration in respiratory status requiring RRT/CA in a large number of non-ICU patients. Patients admitted at a non-ICU level of care suffered rapid clinical deterioration, often with a sudden decrease in oxygen saturation. These patients could benefit from additional monitoring (eg, continuous central oxygenation saturation), although this approach warrants further study.


Subject(s)
Coronavirus Infections/mortality , Demography , Pneumonia, Viral/mortality , Adult , Aged , Aged, 80 and over , Betacoronavirus , Cohort Studies , Female , Heart Arrest/epidemiology , Heart Arrest/mortality , Hospital Mortality , Hospital Rapid Response Team , Hospitalization/statistics & numerical data , Humans , Inpatients/statistics & numerical data , Intensive Care Units , Male , Medical Records , Middle Aged , New York/epidemiology , Oxygen/metabolism , Pandemics , Young Adult
5.
PLoS One ; 15(9): e0239647, 2020.
Article in English | MEDLINE | ID: covidwho-792712

ABSTRACT

Severe acute respiratory syndrome-coronavirus 2 (SARS-CoV-2) is the most significant global health crisis of the 21st century. The aim of this study was to develop a model to simulate the effect of undocumented infections, seasonal infectivity, immunity, and non-pharmaceutical interventions (NPIs) on the transmission, morbidity, and mortality of SARS-CoV-2 in New York State (NYS) based on data collected between March 4 and April 28, 2020. Simulations predict that undocumented infections significantly contribute to infectivity, NPIs are effective in reducing morbidity and mortality, and relaxation >50% of NPIs from initial lock-down levels may result in tens-of-thousands more deaths. Endemic infection is likely to occur in the absence of sustained immunity. As a result, until an effective vaccine or other effective pharmaceutical intervention is developed, the risks of significantly reducing NPIs should be carefully considered. This study employs modelling to simulate fundamental characteristics of SARS-CoV-2 transmission, which can help policymakers navigate combating this virus in the coming years.


Subject(s)
Coronavirus Infections/mortality , Pneumonia, Viral/mortality , Undiagnosed Diseases/epidemiology , Betacoronavirus , Computer Simulation , Coronavirus Infections/transmission , Forecasting , Humans , Immunity , Models, Theoretical , New York/epidemiology , Pandemics , Pneumonia, Viral/transmission , Seasons , Undiagnosed Diseases/virology
7.
PLoS One ; 15(9): e0238827, 2020.
Article in English | MEDLINE | ID: covidwho-751011

ABSTRACT

INTRODUCTION: The role of systemic corticosteroid as a therapeutic agent for patients with COVID-19 pneumonia is controversial. OBJECTIVE: The purpose of this study was to evaluate the effect of corticosteroids in non-intensive care unit (ICU) patients with COVID-19 pneumonia complicated by acute hypoxemic respiratory failure (AHRF). METHODS: This was a single-center retrospective cohort study, from 16th March, 2020 to 30th April, 2020; final follow-up on 10th May, 2020. 265 patients consecutively admitted to the non-ICU wards with laboratory-confirmed COVID-19 pneumonia were screened for inclusion. 205 patients who developed AHRF (SpO2/FiO2 ≤ 440 or PaO2/FiO2 ≤ 300) were only included in the final study. Direct admission to the Intensive care unit (ICU), patients developing composite primary outcome within 24 hours of admission, and patients who never became hypoxic during their stay in the hospital were excluded. Patients were divided into two cohorts based on corticosteroid. The primary outcome was a composite of ICU transfer, intubation, or in-hospital mortality. Secondary outcomes were ICU transfer, intubation, in-hospital mortality, discharge, length of stay, and daily trend of SpO2/FiO2 (SF) ratio from the index date. Cox-proportional hazard regression was implemented to analyze the time to event outcomes. RESULT: Among 205 patients, 60 (29.27%) were treated with corticosteroid. The mean age was ~57 years, and ~75% were men. Thirteen patients (22.41%) developed a primary composite outcome in the corticosteroid cohort vs. 54 (37.5%) patients in the non-corticosteroid cohort (P = 0.039). The adjusted hazard ratio (HR) for the development of the composite primary outcome was 0.15 (95% CI, 0.07-0.33; P <0.001). The adjusted hazard ratio for ICU transfer was 0.16 (95% CI, 0.07 to 0.34; P < 0.001), intubation was 0.31 (95% CI, 0.14 to 0.70; P- 0.005), death was 0.53 (95% CI, 0.22 to 1.31; P- 0.172), composite of death or intubation was 0.31 (95% CI, 0.15 to 0.66; P- 0.002) and discharge was 3.65 (95% CI, 2.20 to 6.06; P<0.001). The corticosteroid cohort had increasing SpO2/FiO2 over time compared to the non-corticosteroid cohort who experience decreasing SpO2/FiO2 over time. CONCLUSION: Among non-ICU patients hospitalized with COVID-19 pneumonia complicated by AHRF, treatment with corticosteroid was associated with a significantly lower risk of the primary composite outcome of ICU transfer, intubation, or in-hospital death, composite of intubation or death and individual components of the primary outcome.


Subject(s)
Adrenal Cortex Hormones/therapeutic use , Coronavirus Infections/drug therapy , Pneumonia, Viral/drug therapy , Adult , Aged , Betacoronavirus/isolation & purification , Coronavirus Infections/mortality , Coronavirus Infections/virology , Female , Hospital Mortality , Humans , Intensive Care Units , Kaplan-Meier Estimate , Male , Middle Aged , New York , Pandemics , Pneumonia, Viral/mortality , Pneumonia, Viral/virology , Proportional Hazards Models , Respiratory Distress Syndrome, Adult/diagnosis , Respiratory Distress Syndrome, Adult/etiology , Retrospective Studies , Treatment Outcome
8.
PLoS One ; 15(9): e0238560, 2020.
Article in English | MEDLINE | ID: covidwho-740405

ABSTRACT

We illustrate and study the evolution of reported infections over the month of March in New York State as a whole, as well as in each individual county in the state. We identify piecewise exponential trends, and search for correlations between the timing and dynamics of these trends and statewide mandated measures on testing and social distancing. We conclude that the reports on April 1 may be dramatically under-representing the actual number of statewide infections, an idea which is supported by more recent retroactive estimates based on serological studies. A follow-up study is underway, reassessing data until June 1, using additional measures for validation and monitoring for effects of the PAUSE directive, and of the reopening timeline.


Subject(s)
Coronavirus Infections/epidemiology , Pneumonia, Viral/epidemiology , Behavior , Betacoronavirus/isolation & purification , Community Participation , Coronavirus Infections/pathology , Coronavirus Infections/virology , Disease Outbreaks , Follow-Up Studies , Hospitalization , Humans , New York/epidemiology , Pandemics , Pneumonia, Viral/pathology , Pneumonia, Viral/virology
9.
Eur J Epidemiol ; 35(8): 733-742, 2020 Aug.
Article in English | MEDLINE | ID: covidwho-708706

ABSTRACT

Forecasting models have been influential in shaping decision-making in the COVID-19 pandemic. However, there is concern that their predictions may have been misleading. Here, we dissect the predictions made by four models for the daily COVID-19 death counts between March 25 and June 5 in New York state, as well as the predictions of ICU bed utilisation made by the influential IHME model. We evaluated the accuracy of the point estimates and the accuracy of the uncertainty estimates of the model predictions. First, we compared the "ground truth" data sources on daily deaths against which these models were trained. Three different data sources were used by these models, and these had substantial differences in recorded daily death counts. Two additional data sources that we examined also provided different death counts per day. For accuracy of prediction, all models fared very poorly. Only 10.2% of the predictions fell within 10% of their training ground truth, irrespective of distance into the future. For accurate assessment of uncertainty, only one model matched relatively well the nominal 95% coverage, but that model did not start predictions until April 16, thus had no impact on early, major decisions. For ICU bed utilisation, the IHME model was highly inaccurate; the point estimates only started to match ground truth after the pandemic wave had started to wane. We conclude that trustworthy models require trustworthy input data to be trained upon. Moreover, models need to be subjected to prespecified real time performance tests, before their results are provided to policy makers and public health officials.


Subject(s)
Coronavirus Infections/mortality , Forecasting/methods , Intensive Care Units/statistics & numerical data , Pandemics/prevention & control , Pneumonia, Viral/mortality , Bed Occupancy , Betacoronavirus , Humans , Intensive Care Units/supply & distribution , Models, Statistical , Mortality/trends , New York/epidemiology , Public Health
10.
Pediatrics ; 146(1)2020 07.
Article in English | MEDLINE | ID: covidwho-701129

ABSTRACT

We describe 3 febrile infants <2 months of age admitted to a large tertiary care children's hospital in New York and subsequently found to be infected with severe acute respiratory syndrome coronavirus 2. All 3 patients presented with fever, feeding difficulty, lymphopenia, and thrombocytosis on laboratory evaluation. Two of the 3 patients were found to have neutropenia, and 2 had known exposures to sick contacts. In this case series, we describe 3 of the youngest patients to be reported with severe acute respiratory syndrome coronavirus 2 in the United States.


Subject(s)
Betacoronavirus , Coronavirus Infections/complications , Coronavirus Infections/diagnosis , Fever/complications , Fever/diagnosis , Pneumonia, Viral/complications , Pneumonia, Viral/diagnosis , Coronavirus Infections/metabolism , Female , Fever/metabolism , Humans , Infant , Infant, Newborn , Male , New York , Pandemics , Pneumonia, Viral/metabolism , Tertiary Care Centers
11.
Stroke ; 51(9): e227-e231, 2020 09.
Article in English | MEDLINE | ID: covidwho-695959

ABSTRACT

BACKGROUND AND PURPOSE: Coronavirus disease 2019 (COVID-19) evolved quickly into a global pandemic with myriad systemic complications, including stroke. We report the largest case series to date of cerebrovascular complications of COVID-19 and compare with stroke patients without infection. METHODS: Retrospective case series of COVID-19 patients with imaging-confirmed stroke, treated at 11 hospitals in New York, between March 14 and April 26, 2020. Demographic, clinical, laboratory, imaging, and outcome data were collected, and cases were compared with date-matched controls without COVID-19 from 1 year prior. RESULTS: Eighty-six COVID-19-positive stroke cases were identified (mean age, 67.4 years; 44.2% women). Ischemic stroke (83.7%) and nonfocal neurological presentations (67.4%) predominated, commonly involving multivascular distributions (45.8%) with associated hemorrhage (20.8%). Compared with controls (n=499), COVID-19 was associated with in-hospital stroke onset (47.7% versus 5.0%; P<0.001), mortality (29.1% versus 9.0%; P<0.001), and Black/multiracial race (58.1% versus 36.9%; P=0.001). COVID-19 was the strongest independent risk factor for in-hospital stroke (odds ratio, 20.9 [95% CI, 10.4-42.2]; P<0.001), whereas COVID-19, older age, and intracranial hemorrhage independently predicted mortality. CONCLUSIONS: COVID-19 is an independent risk factor for stroke in hospitalized patients and mortality, and stroke presentations are frequently atypical.


Subject(s)
Cerebrovascular Disorders/etiology , Coronavirus Infections/complications , Pneumonia, Viral/complications , Adult , Age Factors , Aged , Aged, 80 and over , Brain Ischemia/etiology , Brain Ischemia/therapy , Cerebral Angiography , Cerebrovascular Disorders/mortality , Cerebrovascular Disorders/therapy , Coronavirus Infections/mortality , Coronavirus Infections/therapy , Ethnic Groups , Female , Hospital Mortality , Humans , Intracranial Hemorrhages/complications , Intracranial Hemorrhages/mortality , Male , Middle Aged , Neuroimaging , New York/epidemiology , Pandemics , Pneumonia, Viral/mortality , Pneumonia, Viral/therapy , Retrospective Studies , Risk Factors , Stroke/etiology , Stroke/therapy , Treatment Outcome
12.
Orthopedics ; 43(5): 292-294, 2020 Sep 01.
Article in English | MEDLINE | ID: covidwho-693528

ABSTRACT

The COVID-19 pandemic has had a strong impact on the care of orthopedic patients. This impact has been particularly difficult in New York State, which experienced the largest number of COVID-19 cases and led to a state- mandated pause on all elective surgeries. As a result, physical and occupational therapists became the principal providers of care and had to adjust their workflow to ensure quality care. Understanding the perspectives and needs of therapists relative to the circumstances created by COVID-19 is critical to safe and effective care. The goal of this study was to define the perspectives of therapists in New York State regarding the impact of COVID-19 on their work. An email-based 20-question survey was distributed to 250 therapists from all 10 regions of New York State who treated outpatient orthopedic patients during the peak of the pandemic in early April 2020. The survey collected demographic and practice information as well as responses regarding several clinical practice issues. The results provide insight into the concerns of therapists regarding the delivery of care, and responses clarify indications for therapy and for the use of telemedicine to achieve goals during the pandemic. The COVID-19 pandemic is profoundly impacting the work of therapists worldwide. Therapists responded to this survey expressing concerns about the safe delivery of care, access to personal protective equipment, use of telemedicine, and their role within health care during the pandemic. The results of this study can be used to establish guidelines for safe, effective, and efficient therapy during the pandemic. [Orthopedics. 2020;43(5):292-294.].


Subject(s)
Attitude of Health Personnel , Coronavirus Infections/epidemiology , Orthopedic Procedures/rehabilitation , Orthopedics/trends , Outpatients , Physical Therapy Specialty/trends , Pneumonia, Viral/epidemiology , Telerehabilitation/trends , Betacoronavirus , Elective Surgical Procedures/rehabilitation , Electronic Mail , Humans , New York/epidemiology , Occupational Exposure , Occupational Therapy , Pandemics , Personal Protective Equipment , Surveys and Questionnaires , Telerehabilitation/statistics & numerical data
14.
Am J Nephrol ; 51(8): 669-674, 2020.
Article in English | MEDLINE | ID: covidwho-691050

ABSTRACT

BACKGROUND: The COVID-19 pandemic has affected the end-stage kidney disease (ESKD) population, with high mortality rates reported among patients on hemodialysis. However, the degree to which it has affected the peritoneal dialysis (PD) population in the United States has not yet been elucidated. In this report, we describe the clinical characteristics, presentations, clinical course, and outcomes of ESKD patients on PD hospitalized with COVID-19. METHODS: We describe the characteristics, presentation, and outcomes of adult ESKD patients on chronic PD hospitalized with CO-VID-19 in our 13 major hospitals in the NY health system using descriptive statistical analysis. RESULTS: Of 419 hospitalized patients with ESKD, 11 were on chronic PD therapy (2.6%). Among those 11, 3 patients required mechanical ventilation, 2 of whom died. Of the entire cohort, 9 of the 11 patients (82%) were discharged alive. While fever was a common presentation, more than half of our patients also presented with diarrhea. Interestingly, 3 patients were diagnosed with culture-negative peritonitis during their hospitalization. Seven patients reported positive SARS-CoV-2 exposure from a member of their household. CONCLUSION: Hospitalized patients on PD with COVID-19 had a relatively mild course, and majority of them were discharged home.


Subject(s)
Betacoronavirus/isolation & purification , Coronavirus Infections/epidemiology , Kidney Failure, Chronic/therapy , Peritoneal Dialysis/adverse effects , Peritonitis/epidemiology , Pneumonia, Viral/epidemiology , Adult , Aged , Betacoronavirus/genetics , Coronavirus Infections/complications , Coronavirus Infections/diagnosis , Coronavirus Infections/virology , Electronic Health Records/statistics & numerical data , Female , Hospital Mortality , Hospitalization/statistics & numerical data , Humans , Kidney Failure, Chronic/complications , Kidney Failure, Chronic/mortality , Male , Middle Aged , New York/epidemiology , Pandemics , Peritonitis/diagnosis , Pneumonia, Viral/complications , Pneumonia, Viral/diagnosis , Pneumonia, Viral/virology , RNA, Viral/isolation & purification
16.
PLoS One ; 15(7): e0236618, 2020.
Article in English | MEDLINE | ID: covidwho-691336

ABSTRACT

This study aimed to develop risk scores based on clinical characteristics at presentation to predict intensive care unit (ICU) admission and mortality in COVID-19 patients. 641 hospitalized patients with laboratory-confirmed COVID-19 were selected from 4997 persons under investigation. We performed a retrospective review of medical records of demographics, comorbidities and laboratory tests at the initial presentation. Primary outcomes were ICU admission and death. Logistic regression was used to identify independent clinical variables predicting the two outcomes. The model was validated by splitting the data into 70% for training and 30% for testing. Performance accuracy was evaluated using area under the curve (AUC) of the receiver operating characteristic analysis (ROC). Five significant variables predicting ICU admission were lactate dehydrogenase, procalcitonin, pulse oxygen saturation, smoking history, and lymphocyte count. Seven significant variables predicting mortality were heart failure, procalcitonin, lactate dehydrogenase, chronic obstructive pulmonary disease, pulse oxygen saturation, heart rate, and age. The mortality group uniquely contained cardiopulmonary variables. The risk score model yielded good accuracy with an AUC of 0.74 ([95% CI, 0.63-0.85], p = 0.001) for predicting ICU admission and 0.83 ([95% CI, 0.73-0.92], p<0.001) for predicting mortality for the testing dataset. This study identified key independent clinical variables that predicted ICU admission and mortality associated with COVID-19. This risk score system may prove useful for frontline physicians in clinical decision-making under time-sensitive and resource-constrained environment.


Subject(s)
Betacoronavirus , Coronavirus Infections/epidemiology , Coronavirus Infections/mortality , Intensive Care Units , Models, Theoretical , Patient Admission/trends , Pneumonia, Viral/epidemiology , Pneumonia, Viral/mortality , Aged , Aged, 80 and over , Area Under Curve , Clinical Decision-Making , Coronavirus Infections/virology , Female , Hospitals, University , Humans , Logistic Models , Male , Middle Aged , New York/epidemiology , Pandemics , Pneumonia, Viral/virology , Prognosis , ROC Curve , Retrospective Studies , Risk Factors
18.
J Clin Microbiol ; 58(8)2020 Jul 23.
Article in English | MEDLINE | ID: covidwho-684350

ABSTRACT

Molecular testing for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the gold standard for diagnosis of coronavirus disease 2019 (COVID-19), but the clinical performance of these tests is still poorly understood, particularly with regard to disease course, patient-specific factors, and viral shedding. From 10 March to 1 May 2020, NewYork-Presbyterian laboratories performed 27,377 SARS-CoV-2 molecular assays from 22,338 patients. Repeat testing was performed for 3,432 patients, of which 2,413 had initial negative and 802 had initial positive results. Repeat-tested patients were more likely to have severe disease and low viral loads. The negative predictive value of the first-day result among repeat-tested patients was 81.3% The clinical sensitivity of SARS-CoV-2 molecular assays was estimated between 58% and 96%, depending on the unknown number of false-negative results in single-tested patients. Conversion to negative was unlikely to occur before 15 to 20 days after initial testing or 20 to 30 days after the onset of symptoms, with 50% conversion occurring at 28 days after initial testing. Conversion from first-day negative to positive results increased linearly with each day of testing, reaching 25% probability in 20 days. Sixty patients fluctuated between positive and negative results over several weeks, suggesting that caution is needed when single-test results are acted upon. In summary, our study provides estimates of the clinical performance of SARS-CoV-2 molecular assays and suggests time frames for appropriate repeat testing, namely, 15 to 20 days after a positive test and the same day or next 2 days after a negative test for patients with high suspicion for COVID-19.


Subject(s)
Betacoronavirus/isolation & purification , Clinical Laboratory Techniques/methods , Coronavirus Infections/diagnosis , Diagnostic Tests, Routine/methods , Pneumonia, Viral/diagnosis , Adolescent , Adult , Aged , Aged, 80 and over , Betacoronavirus/genetics , Child , Child, Preschool , Coronavirus Infections/pathology , Coronavirus Infections/virology , Female , Humans , Infant , Infant, Newborn , Male , Middle Aged , New York , Pandemics , Pneumonia, Viral/pathology , Pneumonia, Viral/virology , Predictive Value of Tests , Sensitivity and Specificity , Viral Load , Young Adult
20.
Am J Bioeth ; 20(7): 153-155, 2020 07.
Article in English | MEDLINE | ID: covidwho-679479
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