key: cord-333459-asem8xjz
authors: Mallipattu, S K; Jawa, R; Moffit, R; Hajagos, J; Fries, B; Nachman, S; Gan, T J; Saltz, M; Saltz, J; Kaushansky, K; Skopicki, H; Abell-Hart, K; Chaudhri, I; Deng, J; Garcia, V; Gayen, S; Kurc, T; Bolotova, O; Yoo, J; Dhaliwal, S; Nataraj, N; Sun, S; Tsai, C; Wang, Y; Abbasi, S; Abdullah, R; Ahmad, S; Bai, K; Bennett-Guerrero, E; Chua, A; Gomes, C; Griffel, M; Kalogeropoulos, A; Kiamanesh, D; Kim, N; Koraishy, F; Lingham, V; Mansour, M; Marcos, L; Miller, J; Poovathor, S; Rubano, J; Rutigliano, D; Sands, M; Santora, C; Schwartz, J; Shroyer, K; Skopicki, H; Spitzer, S; Stopeck, A; Talamini, M; Tharakan, M; Vosswinkel, J; Wertheim, W
title: Geospatial Distribution and Predictors of Mortality in Hospitalized Patients with COVID-19: A Cohort Study
date: 2020-09-14
journal: Open Forum Infect Dis
DOI: 10.1093/ofid/ofaa436
sha: 
doc_id: 333459
cord_uid: asem8xjz

BACKGROUND: The global Coronavirus Disease 2019 (COVID-19) pandemic offers the opportunity to assess how hospitals managed the care of hospitalized patients with varying demographics and clinical presentation. The goal of this study is to demonstrate the impact of densely populated residential areas on hospitalization and to identify predictors of length of stay and mortality in hospitalized patients with COVID-19 in one of the hardest hit counties internationally. METHODS: This is a single-center cohort study of 1325 sequentially hospitalized patients with COVID-19 in New York between March 2, 2020 to May 11, 2020. Geospatial distribution of study patients’ residence relative to population density in the region were mapped and data analysis included hospital length of stay, need and duration of invasive mechanical ventilation (IMV), and mortality. Logistic regression models were constructed to predict discharge dispositions in the remaining active study patients. RESULTS: The median age of the study cohort was 62 years (IQR - 49-75), and more than half were male (57%) with history of hypertension (60%), obesity (41%), and diabetes (42%). Geographic residence of the study patients was disproportionately associated with areas of higher population density (r(s)=0.235, p=0.004), with noted “hot spots” in the region. Study patients were predominantly hypertensive (MAP>90mmHg (670, 51%)) on presentation with lymphopenia (590, 55%), hyponatremia (411, 31%), and kidney dysfunction (eGFR&60ml/min/1.73m (2) (381, 29%)). Of the patients with a disposition (1188/1325), 15% (182/1188) required IMV and 21% (250/1188) developed acute kidney injury. In patients on IMV, median hospital length of stay in survivors (22 days; 16.5-29.5) was significantly longer than non-survivors (15 days; 10-23.75), but this was not due to prolonged time on the ventilator. The overall mortality in all hospitalized patients was 15% and in patients receiving IMV was 48%, which is predicted to minimally rise from 48% to 49% based on logistic regression models constructed to project the disposition in the remaining patients on the ventilator. Acute kidney injury during hospitalization (OR(E)=3.23) was the strongest predictor of mortality in patients requiring IMV. CONCLUSIONS: This is the first study to collectively utilize the demographics, clinical characteristics and hospital course of COVID-19 patients to identify predictors of poor outcomes that can be used for resource allocation in future waves of the pandemic.

M a n u s c r i p t

December 2019 in Wuhan, China, the devastating morbidity and mortality coupled with disastrous economic and societal ramifications have characterized this global pandemic [1] .

As of May 11, 2020, New York State far exceeds any other state for the number of individuals infected with Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), with Suffolk County in the top six counties in the entire United States at the time of this report [2] .

The global COVID-19 pandemic offered the opportunity to assess how different geographies managed the care of patients with a predominantly devastating respiratory illness with significant inflammatory, thrombotic, kidney and cardiovascular morbidities.

Suffolk County lies directly east of Nassau County and the greater New York City, and was chronologically later affected by SARS-CoV-2 after its appearance in the five boroughs of New York (Manhattan, Queens, Brooklyn, Staten Island and the Bronx) and Nassau County.

Together Nassau and Suffolk counties constitute the Long Island region, and have approximately similar populations (1,358,564 versus 1,4817,901 people, respectively), albeit with varying population demographics [3] . Recent case series of hospitalized patients in the New York metropolitan area have highlighted the presenting clinical characteristics, morbidity and mortality at medical centers in the region [4, 5] . To allow for comparison of these factors with patient outcomes in one of the most highly affected regions in the world, we report our clinical endpoints based on patient demographics, population density and ethnicity, presenting clinical characteristics and therapeutic interventions for over 1,300 hospitalized patients with confirmed SARS-CoV-2 infection.

A c c e p t e d M a n u s c r i p t 6

The study was conducted at the Renaissance School of Medicine at Stony Brook University, the largest academic medical center in Suffolk County, New York, which provided care for the greatest number of patients with COVID-19 of the eleven hospitals in the county.

Patients hospitalized between March 6, 2020, and May 11, 2020 with COVID-19 as confirmed by at least one positive result for SARS-CoV-2 on PCR testing of nasopharyngeal samples were included in this study. Data was extracted from hospitalized patients from the electronic health record (EHR; Cerner Millennium, Kansas City, MO) and mapped to an Observational Health Data Sciences and Informatics Common Data Model (OHDSI CDM), version 5.3 [6] . Medications were mapped to OHDSI based RxNorm codes and to World Health Organization Anatomical Therapeutic Chemical drug classifications.

Data collection included baseline patient demographic information, comorbidities based on ICD10 codes mapped to Clinical Classification Software (CCS) groups occurring 30 days prior to admission beginning January 1, 2017, admission vital signs, and initial laboratory tests. Race and ethnicity were based on self-reporting at the time of registration and mapped to broader categories using relationships in the OHDSI controlled vocabulary.

Patient locations were geolocated to latitude and longitude using the most recent patient addresses in the EHR with Easy Geocoder [7] . Total population at the Census Tract level was based on the American Community Survey (ACS) 2018 5-year estimates [3]. Population density was calculated using the Tiger shape file estimates for land area in a Census Tract.

Initial laboratory testing was defined as the first test results available within 48 hours of admission. Total patients with available laboratory values are described after exclusion of A c c e p t e d M a n u s c r i p t 7 outlier values. For each of the laboratory values and vital signs provided, we reviewed histograms and defined a range of presumed "valid" measurements (for example, 2 patients had recorded respiratory rates above 150 breaths per minute (bpm) and 2 had values below 6 bpm) and values outside of these ranges were replaced as "missing" for all subsequent analysis. Acute kidney injury during hospitalization was defined as an increase in serum creatinine of 0.3 mg/dL within 48 hours [8] . Estimated glomerular filtration (eGFR) was calculated using the Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) equation [9] . Kidney replacement therapy was defined as onset of hemodialysis (HD) and/or A c c e p t e d M a n u s c r i p t 8

A student"s t test was used to compare continuous data between two groups. Chi-square or Fisher"s Exact testing, as appropriate, was used to compare the significance between categorical variables. For continuous variables, data are expressed as interquartile range (IQR) (25th and 75th percentiles) and/or the number and percentage of patients for categorical variables. Statistical significance was defined as p < 0.05. All analyses were performed using the R programming language (R Project for Statistical Computing; R Foundation).

Logistic regression models were constructed to predict discharge dispositions in our patient population (excluding patients that remained actively hospitalized at the end of the study period)-one model used only variables available at the beginning of an encounter to predict overall outcomes and a second model included more variables in an attempt to find associations with outcomes in mechanically ventilated patients. For each model, we constructed a parsimonious logistic regression with a penalty on the sum of coefficients, i.e. LASSO, using the glmnet package in R. Models were cross-validated and trained on the set of cases with complete, non-missing, values for all variables considered. Our models were tested on the remaining patient data, after missing values were replaced using Multivariate Imputation by Chained Equations, as implemented in the mice package in R.

A total of 2918 SARS-CoV-2 infected patients were seen in our Emergency Department, with 1580 patients discharged to home quarantine for recovery and 1338 patients admitted for hospitalization. Demographics of real-time PCR confirmed SARS-CoV-2 hospitalized patients ≥ 16 years of age (1325/1338) are presented in Table 1 . Among the 767 patients with comorbidities identified during a prior visit, 60% had more than one A c c e p t e d M a n u s c r i p t 9 comorbidity with a predominance of hypertension (464/767, 60%), obesity (434/1065, 41%), and diabetes (324/767, 42%). Figure 1 , demonstrating the geographic residence, the number of hospitalized COVID-19 patient cases, and the population density per census tract (persons per km 2 area). Furthermore, the number of hospitalized COVID-19 patient cases per 10,000 persons was associated with higher population density per kilometer area (r s =0.235, p=0.004) when census tracts centroids were restricted to a 20 km 2 distance (catchment area) from the medical center.

Vital signs on presentation are shown in Supplemental Table 1 and are noteworthy for diastolic hypertension as evidenced by a MAP > 90 mmHg (670/1322, 51%) and SBP > 139 mmHg (362/1322, 27%).

Laboratory data was available for > 90% of the laboratory investigations performed in the study cohort, exceptions noted in Supplemental Table 1 . A significant proportion of the cohort were lymphopenic, as defined by absolute lymphocyte count < 1000 cells/uL, 55%

(590/1081) and hyponatremic, as defined by serum sodium < 135 mEq/L (411/1310, 31%).

End-organ dysfunction was noted on admission with approximately 30% (381/1287) of study cohort with evidence of kidney dysfunction (eGFR 60 ml/min/1.73m 2 ), myocardial injury as measured by elevation in Troponin T (257/1107, 23%)), or hepatic injury with elevation in transaminases (AST (632/1287, 51%) and ALT (239/1287, 19%)). A majority of the cohort also had procalcitonin levels 0.25 (795/1228, 65%) on presentation, suggesting the absence of concomitant superimposed bacterial infection at the start of the disease (Supplemental Table 1 ).

A c c e p t e d M a n u s c r i p t 10

Of the 1325 patients studied, 1011 patients were discharged alive, 177 patients died, and 137 patients remain hospitalized. Discharge disposition, as well as length of stay for those who died or were discharged alive (1188/1325), are presented in Figure 2 and Supplemental Table 2 . Among these 1188 patients with a disposition, the overall mortality was significantly higher in males (p=0.016). This was most prominent in age groups 70-79

and 80-89 years, and that difference confers an advantage to females in whom the death rate does not exceed 10% until age 70, whereas in males that threshold was crossed at age 60.

Overall, the median hospital length of stay (LOS) was significantly longer in patients who died (10 days; IQR 4-16) as compared to patients discharged alive (8 days; IQR 5-12;

p=0.0024) ( Figure 3A , Supplemental Table 3 ). No such difference in hospital length of stay was noted for patients who did not receive IMV (Supplemental Figure 1 , Figure 3B (bottom panel)). Interestingly, the overall hospital LOS in patients on IMV was significantly longer in those discharged alive (22 days; IQR 16.5-29.5) as compared to those that died (15 days; IQR 10-23.75; p=3.84e-07) ( Figure 3B, top panel) . However, length of time on the ventilator in patients discharged alive (10 days; IQR 6-13) was slightly shorter from those that died (10 days; IQR 6.5-17.5; p=0.04) due to skewing of the interquartile range in the deceased population ( Figure 3C) . These data suggest that the longer hospital length of stay in patients discharged alive after being on IMV is attributable to a greater number of nonventilator hospital days. It is noteworthy that in the remaining active patients in the hospital by the end of the study period, the duration of invasive mechanical ventilatory support was significantly prolonged as compared to the patients with a disposition by the study end point ( Figure 3C) .

A c c e p t e d M a n u s c r i p t 11

Of 1188 patients with a disposition, 182 (15%) patients required IMV (Supplemental Table 4 ), with a majority of these patients (52%) eventually discharged alive. Acute kidney injury developed in 21% (250/1188) of the patients with approximately 26% (64/250) of these patients requiring kidney replacement therapy (CRRT or HD) (Supplemental Table 4 ). Fiftythree percent (34/64) of patients who received kidney replacement therapy were eventually discharged alive (Supplemental Table 4 ).

Approximately 7.5% (89/1188) of our encounters represented readmissions, and the majority of these readmitted patients were eventually discharged alive (83/89, 91%).

Furthermore, of 1011 individuals that were discharged alive, approximately 77% (773/1011) were discharged home as compared to a rehabilitation or skilled nursing facility (Supplemental Table 4 ). Finally, the overall mortality was 15% (177/1188) in all hospitalized patients and 48% (87/182) in patients on IMV with a disposition.

A univariate analysis performed with parameters assessed on admission demonstrated that age 65 years and male gender as well as several comorbidities were associated with mortality (Supplemental Table 5 ). Clinical measures such as the need for IMV and kidney replacement therapy were associated with mortality, with acute kidney injury during hospitalization as having the highest odds ratio (Supplemental Table 5 A c c e p t e d M a n u s c r i p t 12 predicted lower survival in these patients, if found on initial presentation (Supplemental Table 6 ).

In patients who received IMV, only age 65 years, male gender, comorbidities (hypertension, heart failure), need for kidney replacement therapy, and acute kidney injury during hospitalization were significantly associated with increased mortality in the univariate analysis (Supplemental Table 5 ). Subsequent multivariate modeling in these patients demonstrated that, of the variables individually associated with outcomes, only male gender, older age, history of heart failure, and acute kidney injury during hospitalization were predictive of mortality; with acute kidney injury remaining as the most important predictor in this analysis (Table 2) .

Despite multiple therapeutic interventions; hydroxychloroquine, azithromycin, therapeutic anticoagulation, tocilizumab, zinc, thiamine, and ascorbic acid, none were associated with improved survival in the patients on IMV (Supplemental Table 5 ). However, we observed a trend toward improved survival in patients on IMV that received therapeutic enoxaparin as compared to intravenous unfractionated heparin.

A logistic regression model was constructed to predict discharge disposition in the remaining active 67 patients that received IMV, with noted characteristics of the predictive performance (Supplemental Figure 2) . The mortality in these patients is projected to rise from 48% to 49%, based on the prediction that 36 patients are likely to expire as compared to 31 patients that are likely to be discharged alive in the remaining patients on mechanical ventilation. The observation that the geographic residence of the hospitalized COVID-19 patient cases was disproportionately associated with higher population density is not surprising, in light of the imperative to maintain physical distancing to reduce transmission and mitigate the peak intensity of this pandemic. While our medical center catchment area overlaps with several medical centers in the county, the identification of these "hot-spots" will be essential for future resource allocation planning. Investigating the potential cofounders, including comorbidities, socioeconomic status, occupational exposure, education level and ethnicallybased differences contained within these geographic spaces are also critical for future responses.

Over 75% of hospitalized patients (with a documented previous visit to our system) had at least one comorbidity, with 60% of individuals reporting more than one comorbidity.

While our findings support those of studies in the New York City area [1, 4] , we observed much higher rates of these comorbidities than the original Wuhan study 1 , and a metaanalysis of approximately 3500 patients from China [1, 4, 11] . This likely represents our older patient cohort, but may also represent underlying differences in the prevalence of these A c c e p t e d M a n u s c r i p t 14 diseases in different countries as well as the detection and definition of disease during previous visits to our system.

While, the history of cancer, pre-existing diabetes and heart failure were independent predictors of mortality on presentation, the presence of pre-existing heart failure alone predicted mortality in patients requiring IMV. Our model revealed that other strong predictors for mortality in mechanically ventilated COVID19 patients were older age, male gender, and acute kidney injury during hospitalization. Still, these features remain correlative, making it difficult to assign relative importance to any one factor. However, our model of mortality on presentation used only basic data available in the first 48 hours of admission and achieved a 91% positive predictive value for survival (AUROC 83%, sensitivity 90%, specificity 54%).

Additional studies are required to determine if these observations can be validated in cohorts with differing demographics.

The lack of an overwhelming clinical response to the evolving treatment strategies Not surprising, the overall hospital length of stay was longer in individuals who died as compared to those that survived. However, in patients that required IMV, we observed the opposite; the overall hospital length of stay was significantly longer in those that survived as compared to those that died. Interestingly, this prolonged hospital course was not driven by the duration of mechanical ventilation, but rather by hospitalization events pre-dating or post- M a n u s c r i p t 23 

COVID-19 Data Analysis Group

COVID-19 Clinical Coordinating Group

Clinical Characteristics of Coronavirus Disease 2019 in China

Coronavirus COVID-19 global cases

Detailed Tables; generated by Janos Hajagos

Outcomes Among 5700 Patients Hospitalized With COVID-19 in the New York City Area

Hospitalizations and Deaths Across New York City Boroughs

Feasibility and utility of applications of the common data model to multiple, disparate observational health databases

Effective Scalable and Integrative Geocoding for Massive Address Datasets

KDIGO clinical practice guidelines for acute kidney injury

A new equation to estimate glomerular filtration rate

Characteristics and Outcomes of 21 Critically Ill Patients With COVID-19 in Washington State

Prevalence of Underlying Diseases in Hospitalized Patients with COVID-19: a Systematic Review and Meta-Analysis

National Institutes of Health

Association of Treatment With Hydroxychloroquine or Azithromycin With In-Hospital Mortality in Patients With COVID-19 in New York State

A c c e p t e d M a n u s c r i p t 18 FUNDING None

The authors declare that they have no competing interests A c c e p t e d M a n u s c r i p t 19 A c c e p t e d M a n u s c r i p t