scholarly journals Acute Kidney Injury in Patients Hospitalized With COVID-19 in New York City: Temporal Trends From March 2020 to April 2021

2021 ◽  
Author(s):  
Sergio Dellepiane ◽  
Akhil Vaid ◽  
Suraj K. Jaladanki ◽  
Steven Coca ◽  
Zahi A. Fayad ◽  
...  
Keyword(s):  
New York ◽  
Acute Kidney Injury ◽  
New York City ◽  
York City ◽  
Temporal Trends ◽  
Kidney Injury

scholarly journals Temporal Trends in COVID-19 associated AKI from March to December 2020 in New York City

2021 ◽  
Author(s):  
Sergio Dellepiane ◽  
Akhil Vaid ◽  
Suraj K Jaladanki ◽  
Ishan Paranjpe ◽  
Steven Coca ◽  
...  
Keyword(s):  
United States ◽  
New York ◽  
Acute Kidney Injury ◽  
New York City ◽  
Renal Replacement Therapy ◽  
York City ◽  
Temporal Trends ◽  
Kidney Injury ◽  
Clinical Approach ◽  
Over Time

AbstractAcute Kidney Injury (AKI) is among the most common complications of Coronavirus Disease 2019 (COVID-19). Throughout 2020 pandemic, the clinical approach to COVID-19 has progressively improved, but it is unknown how these changes have affected AKI incidence and severity. In this retrospective analysis, we report the trend over time of COVID-19 associated AKI and need of renal replacement therapy in a large health system in New York City, the first COVID-19 epicenter in United States.

scholarly journals Development of a federated learning approach to predict acute kidney injury in adult hospitalized patients with COVID-19 in New York City

2021 ◽  
Author(s):  
Suraj K Jaladanki ◽  
Akhil Vaid ◽  
Ashwin S Sawant ◽  
Jie Xu ◽  
Kush Shah ◽  
...  
Keyword(s):  
New York ◽  
Acute Kidney Injury ◽  
New York City ◽  
York City ◽  
Data Security ◽  
Data Privacy ◽  
Vital Signs ◽  
Kidney Injury ◽  
Pooled Data ◽  
Level Data

Federated learning is a technique for training predictive models without sharing patient-level data, thus maintaining data security while allowing inter-institutional collaboration. We used federated learning to predict acute kidney injury within three and seven days of admission, using demographics, comorbidities, vital signs, and laboratory values, in 4029 adults hospitalized with COVID-19 at five sociodemographically diverse New York City hospitals, between March-October 2020. Prediction performance of federated models was generally higher than single-hospital models and was comparable to pooled-data models. In the first use-case in kidney disease, federated learning improved prediction of a common complication of COVID-19, while preserving data privacy.

scholarly journals Peritoneal Dialysis for Acute Kidney Injury During the COVID-19 Pandemic in New York City

2020 ◽  
Vol 5 (9) ◽  
pp. 1532-1534 ◽  
Author(s):  
Divya Shankaranarayanan ◽  
Sanjay P. Neupane ◽  
Elly Varma ◽  
Daniil Shimonov ◽  
Supriya Gerardine ◽  
...  
Keyword(s):  
New York ◽  
Acute Kidney Injury ◽  
New York City ◽  
Peritoneal Dialysis ◽  
York City ◽  
Kidney Injury

Is There a Limit to Adoption of Dynamic Ridesharing Systems? Evidence from Analysis of Uber Demand Data from New York City

2018 ◽  
Vol 2672 (42) ◽  
pp. 127-136 ◽  
Author(s):  
Raymond Gerte ◽  
Karthik C. Konduri ◽  
Naveen Eluru
Keyword(s):  
New York ◽  
New York City ◽  
York City ◽  
Transportation Networks ◽  
Autonomous Vehicle ◽  
Temporal Trends ◽  
Technological Advances ◽  
Vehicle Operation ◽  
Bike Sharing ◽  
And Shifts

Recent technological advances have paved the way for new mobility alternatives within established transportation networks, including on-demand ride hailing/sharing (e.g., Uber, Lyft) and citywide bike sharing. Common across these innovative modes is a lack of direct ownership by the user; in each of these mobility offerings, a resource not owned by the end users’ is shared for fulfilling travel needs. This concept has flourished and is being hailed as a potential option for autonomous vehicle operation moving forward. However, substantial investigation into how new shared modes affect travel behaviors and integrate into existing transportation networks is lacking. This paper explores whether the growth in the adoption and usage of these modes is unbounded, or if there is a limit to their uptake. Recent trends and shifts in Uber demand usage from New York City were investigated to explore the hypothesis. Using publicly available data about Uber trips, temporal trends in the weekly demand for Uber were explored in the borough of Manhattan. A panel-based random effects model accounting for both heteroscedasticity and autocorrelation effects was estimated wherein weekly demand was expressed as a function of a variety of demographic, land use, and environmental factors. It was observed that demand appeared to initially increase after the introduction of Uber, but seemed to have stagnated and waned over time in heavily residential portions of the island, contradicting the observed macroscopic unbounded growth. The implications extend beyond already existing fully shared systems and also affect the planning of future mobility offerings.

Temporal Trends in Tuberculosis Hospitalization Rates Before and After Implementation of Directly Observed Therapy: New York City, 1988–1995

2002 ◽  
Vol 23 (4) ◽  
pp. 221-223 ◽  
Author(s):  
Mary Beth Terry ◽  
Moïse Desvarieux ◽  
Margaret Short
Keyword(s):  
New York ◽  
New York City ◽  
York City ◽  
Temporal Trends ◽  
Incidence Rates ◽  
Directly Observed Therapy ◽  
Hospitalization Rates ◽  
Widespread Implementation ◽  
Before And After

AbstractNew York City hospitalization rates were analyzed to investigate whether tuberculosis (TB) hospitalizations declined after implementation of directly observed therapy QOOT) for TB. TB hospitalization rates mirrored incidence rates in pattern but not in magnitude. Rates have declined significantly following widespread implementation of DOT in 1993.

Understanding the Relationships between Demand for Shared Ride Modes: Case Study using Open Data from New York City

2019 ◽  
Vol 2673 (12) ◽  
pp. 30-39 ◽  
Author(s):  
Raymond Gerte ◽  
Karthik C. Konduri ◽  
Nalini Ravishanker ◽  
Amit Mondal ◽  
Naveen Eluru
Keyword(s):  
New York ◽  
New York City ◽  
York City ◽  
Transportation Network ◽  
Temporal Trends ◽  
Primary Objective ◽  
Linear Modeling ◽  
Modeling Framework ◽  
Exogenous Factors ◽  
Citi Bike

The concept of shared travel, making trips with other users via a common vehicle, is far from novel. However, a changing technological climate has laid the tracks for new dynamically shared modes in the form of transportation network companies (TNCs), to substantially impact travel behavior. The current body of research on how these modal offerings impact the demand for existing shared modes (e.g., bikeshare, transit) is growing. However, a comprehensive investigation of the temporal evolution of the demand for TNCs and their relationship to other shared modes, is lacking. This research tackles this important limitation by analyzing ridership data for TNCs, taxi, subway, and Citi Bike in New York City using daily ridership data from January 2015 through June 2017. The primary objective was to understand the relationship between TNCs and other shared modal offerings while accounting for the influence of temporal trends and other exogenous factors. A dynamic linear modeling framework was formulated to accommodate time-dependent trends, periodicity, and time-varying exogenous factors on the demand for TNCs. As a preliminary work, the findings of this study reinforce the observed substitution relationship between taxis and TNCs. The results may also indicate a substitutional relationship between TNCs and Citi Bike, and a complementary relationship with subway, however these results still need to be explored further. With potentially impactful findings for planning and policymakers, the predictive model developed in the study can be used to carry out forecasting in support of short- and long-term operations and planning applications.

Drug Use among New York City Prison Inmates: A Demographic Study with Temporal Trends

1991 ◽  
Vol 26 (10) ◽  
pp. 1089-1105 ◽  
Author(s):  
K. H. Van Hoeven ◽  
R. L. Stoneburner ◽  
W. C. Rooney
Keyword(s):  
New York ◽  
New York City ◽  
Drug Use ◽  
York City ◽  
Temporal Trends ◽  
Prison Inmates ◽  
Demographic Study

scholarly journals Acute Peritoneal Dialysis During the COVID-19 Pandemic at Bellevue Hospital in New York City

2020 ◽  
Author(s):  
Nina J Caplin ◽  
Olga Zhdanova ◽  
Manish Tandon ◽  
Nathan Thompson ◽  
Dhwanil Patel ◽  
...  
Keyword(s):  
Intensive Care Unit ◽  
New York ◽  
Acute Kidney Injury ◽  
New York City ◽  
Renal Function ◽  
Intensive Care ◽  
Peritoneal Dialysis ◽  
Kidney Injury ◽  
Bellevue Hospital ◽  
Kidney Replacement Therapy

The COVID-19 pandemic created an unprecedented strain on hospitals in New York City. Although practitioners focused on the pulmonary devastation, resources for the provision of dialysis proved to be more constrained. To deal with these shortfalls, NYC Health and Hospitals/Bellevue, NYU Brooklyn, NYU Medical Center and the New York Harbor VA Healthcare System, put together a plan to offset the anticipated increased needs for kidney replacement therapy. Prior to the pandemic, peritoneal dialysis was not used for acute kidney injury at Bellevue Hospital. We were able to rapidly establish an acute peritoneal dialysis program at Bellevue Hospital for acute kidney injury patients in the intensive care unit. A dedicated surgery team was assembled to work with the nephrologists for bedside placement of the peritoneal dialysis catheters. A multi-disciplinary team was trained by the lead nephrologist to deliver peritoneal dialysis in the intensive care unit. Between April 8, 2020 and May 8, 2020, 39 peritoneal dialysis catheters were placed at Bellevue Hospital. 38 patients were successfully started on peritoneal dialysis. As of June 10, 2020, 16 patients recovered renal function. One end stage kidney disease patient was converted to peritoneal dialysis and was discharged. One catheter was poorly functioning, and the patient was changed to hemodialysis before recovering renal function. There were no episodes of peritonitis and nine incidents of minor leaking, which resolved. Some patients received successful peritoneal dialysis while being ventilated in the prone position. In summary, despite severe shortages of staff, supplies and dialysis machines during the COVID-19 pandemic, we were able to rapidly implement a de novo peritoneal dialysis program which enabled provision of adequate kidney replacement therapy to all admitted patients who needed it. Our experience is a model for the use of acute peritoneal dialysis in crisis situations.

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