Geospatial Distribution and Predictors of Mortality in Hospitalized Patients With COVID-19: A Cohort Study

Dialysis resource allocation in critical care: the impact of the COVID-19 pandemic and the promise of big data analytics

The COVID-19 pandemic resulted in an unprecedented burden on intensive care units (ICUs). With increased demands and limited supply, critical care resources, including dialysis machines, became scarce, leading to the undertaking of value-based cost-effectiveness analyses and the rationing of resources to deliver patient care of the highest quality. A high proportion of COVID-19 patients admitted to the ICU required dialysis, resulting in a major burden on resources such as dialysis machines, nursing staff, technicians, and consumables such as dialysis filters and solutions and anticoagulation medications. Artificial intelligence (AI)-based big data analytics are now being utilized in multiple data-driven healthcare services, including the optimization of healthcare system utilization. Numerous factors can impact dialysis resource allocation to critically ill patients, especially during public health emergencies, but currently, resource allocation is determined using a small number of traditional factors. Smart analytics that take into account all the relevant healthcare information in the hospital system and patient outcomes can lead to improved resource allocation, cost-effectiveness, and quality of care. In this review, we discuss dialysis resource utilization in critical care, the impact of the COVID-19 pandemic, and how AI can improve resource utilization in future public health emergencies. Research in this area should be an important priority.

Geographic and Temporal Trends in COVID-Associated Acute Kidney Injury in the National COVID Cohort Collaborative

BACKGROUND

AKI is associated with mortality in patients hospitalized with coronavirus disease 2019 (COVID-19); however, its incidence, geographic distribution, and temporal trends since the start of the pandemic are understudied.

METHODS

Electronic health record data were obtained from 53 health systems in the United States in the National COVID Cohort Collaborative. We selected hospitalized adults diagnosed with COVID-19 between March 6, 2020, and January 6, 2022. AKI was determined with serum creatinine and diagnosis codes. Time was divided into 16-week periods (P1-6) and geographical regions into Northeast, Midwest, South, and West. Multivariable models were used to analyze the risk factors for AKI or mortality.

RESULTS

Of a total cohort of 336,473, 129,176 (38%) patients had AKI. Fifty-six thousand three hundred and twenty-two (17%) lacked a diagnosis code but had AKI based on the change in serum creatinine. Similar to patients coded for AKI, these patients had higher mortality compared with those without AKI. The incidence of AKI was highest in P1 (47%; 23,097/48,947), lower in P2 (37%; 12,102/32,513), and relatively stable thereafter. Compared with the Midwest, the Northeast, South, and West had higher adjusted odds of AKI in P1. Subsequently, the South and West regions continued to have the highest relative AKI odds. In multivariable models, AKI defined by either serum creatinine or diagnostic code and the severity of AKI was associated with mortality.

CONCLUSIONS

The incidence and distribution of COVID-19-associated AKI changed since the first wave of the pandemic in the United States.

PODCAST

This article contains a podcast at https://dts.podtrac.com/redirect.mp3/www.asn-online.org/media/podcast/CJASN/2023_08_08_CJN0000000000000192.mp3.

COVID-19-associated AKI in hospitalized US patients: incidence, temporal trends, geographical distribution, risk factors and mortality

Background

Acute kidney injury (AKI) is associated with mortality in patients hospitalized with COVID-19, however, its incidence, geographic distribution, and temporal trends since the start of the pandemic are understudied.

Methods

Electronic health record data were obtained from 53 health systems in the United States (US) in the National COVID Cohort Collaborative (N3C). We selected hospitalized adults diagnosed with COVID-19 between March 6th, 2020, and January 6th, 2022. AKI was determined with serum creatinine (SCr) and diagnosis codes. Time were divided into 16-weeks (P1-6) periods and geographical regions into Northeast, Midwest, South, and West. Multivariable models were used to analyze the risk factors for AKI or mortality.

Results

Out of a total cohort of 306,061, 126,478 (41.0 %) patients had AKI. Among these, 17.9% lacked a diagnosis code but had AKI based on the change in SCr. Similar to patients coded for AKI, these patients had higher mortality compared to those without AKI. The incidence of AKI was highest in P1 (49.3%), reduced in P2 (40.6%), and relatively stable thereafter. Compared to the Midwest, the Northeast, South, and West had higher adjusted AKI incidence in P1, subsequently, the South and West regions continued to have the highest relative incidence. In multivariable models, AKI defined by either SCr or diagnostic code, and the severity of AKI was associated with mortality.

Conclusions

Uncoded cases of COVID-19-associated AKI are common and associated with mortality. The incidence and distribution of COVID-19-associated AKI have changed since the first wave of the pandemic in the US.

Comparing body mass index and obesity-related comorbidities as predictors in hospitalized COVID-19 patients

The association between body mass index (BMI) and poor COVID-19 outcomes in patients has been demonstrated across numerous studies. However, obesity-related comorbidities have also been shown to be associated with poor outcomes. The purpose of this study was to determine whether BMI or obesity-associated comorbidities contribute to elevated COVID-19 severity in non-elderly, hospitalized patients with elevated BMI (≥25 kg/m² ). This was a single-center, retrospective cohort study of 526 hospitalized, non-elderly adult (aged 18-64) COVID-19 patients with BMI ≥25 kg/m² in suburban New York from March 6 to May 11, 2020. The Edmonton Obesity Staging System (EOSS) was used to quantify the severity of obesity-related comorbidities. EOSS was compared with BMI in multivariable regression analyses to predict COVID-19 outcomes. We found that higher EOSS scores were associated with poor outcomes after demographic adjustment, unlike BMI. Specifically, patients with increased EOSS scores had increased odds of acute kidney injury (adjusted odds ratio [aOR] = 6.40; 95% CI 3.71-11.05), intensive care unit admission (aOR = 10.71; 95% CI 3.23-35.51), mechanical ventilation (aOR = 3.10; 95% CI 2.01-4.78) and mortality (aOR = 5.05; 95% CI 1.83-13.90). Obesity-related comorbidity burden as determined by EOSS was a better predictor of poor COVID-19 outcomes relative to BMI, suggesting that comorbidity burden may be driving risk in those hospitalized with elevated BMI.

Prevalence and outcomes of hyponatremia among COVID-19 patients: A systematic review and meta-analysis

Objectives

On March 2020, the WHO declared coronavirus disease 2019 (COVID-19) pandemic. COVID-19 is associated with various clinical syndromes, with electrolytes imbalances involved. This review aims to quantify the prevalence and outcomes of hyponatremia among COVID-19 patients, as well as to review the underlying pathophysiological mechanisms of hyponatremia among these patients.

Methods

Using Preferred Reporting Items for Systematic Reviews and Meta Analyses guidelines, we conducted a systematic literature search using the electronic databases of Google Scholar, MEDLINE (PubMed), WHO Virtual Health Library, and ScienceDirect, without limitations regarding gender, geographical area, race or publication date, up until December 13, 2021. Primary outcomes measured were mortality, intensive care unit (ICU) admission, assisted ventilation need, and length of hospital stay (LOS). Secondary outcome was the mechanism underlying hyponatremia among COVID-19 patients.

Results

From a total of 52 included studies, 23 underwent quantitative analysis. For the primary outcomes; proportions, odds ratios (OR), and standardized mean difference (SMD) were calculated using random effects model. The prevalence of hyponatremia was found to be 25.8%. Hyponatremia was found to be significantly associated with increased odds for mortality (OR = 1.97[95% CI, 1.50-2.59]), ICU admission (OR = 1.91 [95% CI, 1.56-2.35]), assisted ventilation need (OR = 2.04 [95% CI, 1.73-2.38]), and with increased LOS (SMD of 5.74 h [95% CI, 0.092-0.385]). Regarding the mechanisms underlying hyponatremia, syndrome of inappropriate anti-diuretic hormone secretion (SIADH) was most commonly reported, followed by adrenal insufficiency, and finally hypovolemic hyponatremia due to gastrointestinal losses.

Conclusion

Hyponatremia among COVID-19 patients is generally associated with poor outcomes, with SIADH being the most common underlying mechanism.

Short- and Long-Term Recovery after Moderate/Severe AKI in Patients with and without COVID-19

Background

Severe AKI is strongly associated with poor outcomes in coronavirus disease 2019 (COVID-19), but data on renal recovery are lacking.

Methods

We retrospectively analyzed these associations in 3299 hospitalized patients (1338 with COVID-19 and 1961 with acute respiratory illness but who tested negative for COVID-19). Uni- and multivariable analyses were used to study mortality and recovery after Kidney Disease Improving Global Outcomes Stages 2 and 3 AKI (AKI-2/3), and Machine Learning was used to predict AKI and recovery using admission data. Long-term renal function and other outcomes were studied in a subgroup of AKI-2/3 survivors.

Results

Among the 172 COVID-19-negative patients with AKI-2/3, 74% had partial and 44% complete renal recovery, whereas 12% died. Among 255 COVID-19 positive patients with AKI-2/3, lower recovery and higher mortality were noted (51% partial renal recovery, 25% complete renal recovery, 24% died). On multivariable analysis, intensive care unit admission and acute respiratory distress syndrome were associated with nonrecovery, and recovery was significantly associated with survival in COVID-19-positive patients. With Machine Learning, we were able to predict recovery from COVID-19-associated AKI-2/3 with an average precision of 0.62, and the strongest predictors of recovery were initial arterial partial pressure of oxygen and carbon dioxide, serum creatinine, potassium, lymphocyte count, and creatine phosphokinase. At 12-month follow-up, among 52 survivors with AKI-2/3, 26% COVID-19-positive and 24% COVID-19-negative patients had incident or progressive CKD.

Conclusions

Recovery from COVID-19-associated moderate/severe AKI can be predicted using admission data and is associated with severity of respiratory disease and in-hospital death. The risk of CKD might be similar between COVID-19-positive and -negative patients.

Recent Advances in Kidney Bioengineering

Kidney disease is an epidemic that affects more than 600 million people worldwide. The socioeconomic impacts of the disease disproportionately affect Hispanic and non-Hispanic Black Americans, making the disease an issue of social inequality. The urgency of this situation has only become worse during the COVID-19 pandemic, as those who are hospitalized for COVID-19 have an increased risk of kidney failure. For researchers, the kidney is a complex organ that is difficult to accurately model and understand. Traditional cell culture models are not adequate for studying the functional intricacies of the kidney, but recent experiments have offered improvements for understanding these systems. Recent progress includes organoid modeling, 3D bioprinting, decellularization, and microfluidics. Here, we offer a review of the most recent advances in kidney bioengineering.

Longer Prehospitalization and Preintubation Periods in Intubated Non-survivors and ECMO Patients With COVID-19: A Systematic Review and Meta-Analysis

Purpose: There is no clear consensus on the clinical course of critical COVID-19 patients. We examined the clinical course among intubated survivors, non-survivors, and extracorporeal membrane oxygenation (ECMO) patients to reveal the standard clinical course and the difference among critical COVID-19 patients.

Methods: In this systematic review and meta-analysis, we searched PubMed, Web of Science, and Scopus for original studies published until December 11, 2020, including case accumulation and clinical course reporting. Pregnant patients and children were excluded. We followed PRISMA guidelines and registered them with PROSPERO (CRD42021235534).

Results: Of the 11,716 studies identified, 94 met the selection criteria, and 2,549 cases were included in this meta-analysis. The times from intubation to extubation and death were 12.07 days (95% confidence interval 9.80–14.33 days) and 10.14 days (8.18–12.10 days), respectively, and the ECMO duration was 14.72 days (10.57–18.87 days). The time from symptom onset to hospitalization (prehospitalization period) of intubated survivors, non-survivors, and ECMO patients was 6.15 (4.61–7.69 days), 6.45 (4.55–8.34 days), and 7.15 days (6.48–7.81 days), and that from symptom onset to intubation (preintubation period) was 8.58 (7.36–9.80 days), 9.14 (7.26–11.01 days), and 10.54 days (9.18–11.90 days), respectively. Sensitivity analysis showed that the time from intubation to extubation and death was longer in the US and Europe than in East Asia.

Conclusion: For COVID-19, we hypothesize that prehospitalization and preintubation periods are longer in intubated non-survivors and ECMO patients than in intubated survivors. These periods may serve as a predictor of disease severity or death and support therapeutic strategy determination.

Diabetes, hypertension, body mass index, smoking and COVID-19-related mortality: a systematic review and meta-analysis of observational studies

OBJECTIVES

We conducted a systematic literature review and meta-analysis of observational studies to investigate the association between diabetes, hypertension, body mass index (BMI) or smoking with the risk of death in patients with COVID-19 and to estimate the proportion of deaths attributable to these conditions.

METHODS

Relevant observational studies were identified by searches in the PubMed, Cochrane library and Embase databases through 14 November 2020. Random-effects models were used to estimate summary relative risks (SRRs) and 95% CIs. Certainty of evidence was assessed using the Cochrane methods and the Grading of Recommendations, Assessment, Development and Evaluations framework.

RESULTS

A total of 186 studies representing 210 447 deaths among 1 304 587 patients with COVID-19 were included in this analysis. The SRR for death in patients with COVID-19 was 1.54 (95% CI 1.44 to 1.64, I2=92%, n=145, low certainty) for diabetes and 1.42 (95% CI 1.30 to 1.54, I2=90%, n=127, low certainty) for hypertension compared with patients without each of these comorbidities. Regarding obesity, the SSR was 1.45 (95% CI 1.31 to 1.61, I2=91%, n=54, high certainty) for patients with BMI ≥30 kg/m2 compared with those with BMI <30 kg/m2 and 1.12 (95% CI 1.07 to 1.17, I2=68%, n=25) per 5 kg/m2 increase in BMI. There was evidence of a J-shaped non-linear dose-response relationship between BMI and mortality from COVID-19, with the nadir of the curve at a BMI of around 22-24, and a 1.5-2-fold increase in COVID-19 mortality with extreme obesity (BMI of 40-45). The SRR was 1.28 (95% CI 1.17 to 1.40, I2=74%, n=28, low certainty) for ever, 1.29 (95% CI 1.03 to 1.62, I2=84%, n=19) for current and 1.25 (95% CI 1.11 to 1.42, I2=75%, n=14) for former smokers compared with never smokers. The absolute risk of COVID-19 death was increased by 14%, 11%, 12% and 7% for diabetes, hypertension, obesity and smoking, respectively. The proportion of deaths attributable to diabetes, hypertension, obesity and smoking was 8%, 7%, 11% and 2%, respectively.

CONCLUSION

Our findings suggest that diabetes, hypertension, obesity and smoking were associated with higher COVID-19 mortality, contributing to nearly 30% of COVID-19 deaths.

TRIAL REGISTRATION NUMBER

CRD42020218115.

A catalogue of tools and variables from crisis and routine care to support decision-making about allocation of intensive care beds and ventilator treatment during pandemics: Scoping review

PURPOSE

This scoping review sought to identify objective factors to assist clinicians and policy-makers in making consistent, objective and ethically sound decisions about resource allocation when healthcare rationing is inevitable.

MATERIALS AND METHODS

Review of guidelines and tools used in ICUs, hospital wards and emergency departments on how to best allocate intensive care beds and ventilators either during routine care or developed during previous epidemics, and association with patient outcomes during and after hospitalisation.

RESULTS

Eighty publications from 20 countries reporting accuracy or validity of prognostic tools/algorithms, or significant correlation between prognostic variables and clinical outcomes met our eligibility criteria: twelve pandemic guidelines/triage protocols/consensus statements, twenty-two pandemic algorithms, and 46 prognostic tools/variables from non-crisis situations. Prognostic indicators presented here can be combined to create locally-relevant triage algorithms for clinicians and policy makers deciding about allocation of ICU beds and ventilators during a pandemic. No consensus was found on the ethical issues to incorporate in the decision to admit or triage out of intensive care.

CONCLUSIONS

This review provides a unique reference intended as a discussion starter for clinicians and policy makers to consider formalising an objective a locally-relevant triage consensus document that enhances confidence in decision-making during healthcare rationing of critical care and ventilator resources.

A longitudinal and geospatial analysis of COVID-19 tweets during the early outbreak period in the United States

INTRODUCTION

Early reports of COVID-19 cases and deaths may not accurately convey community-level concern about the pandemic during early stages, particularly in the United States where testing capacity was initially limited. Social media interaction may elucidate public reaction and communication dynamics about COVID-19 in this critical period, during which communities may have formulated initial conceptions about the perceived severity of the pandemic.

METHODS

Tweets were collected from the Twitter public API stream filtered for keywords related to COVID-19. Using a pre-existing training set, a support vector machine (SVM) classifier was used to obtain a larger set of geocoded tweets with characteristics of user self-reporting COVID-19 symptoms, concerns, and experiences. We then assessed the longitudinal relationship between identified tweets and the number of officially reported COVID-19 cases using linear and exponential regression at the U.S. county level. Changes in tweets that included geospatial clustering were also assessed for the top five most populous U.S. cities.

RESULTS

From an initial dataset of 60 million tweets, we analyzed 459,937 tweets that contained COVID-19-related keywords that were also geolocated to U.S. counties. We observed an increasing number of tweets throughout the study period, although there was variation between city centers and residential areas. Tweets identified as COVID-19 symptoms or concerns appeared to be more predictive of active COVID-19 cases as temporal distance increased.

CONCLUSION

Results from this study suggest that social media communication dynamics during the early stages of a global pandemic may exhibit a number of geospatial-specific variations among different communities and that targeted pandemic communication is warranted. User engagement on COVID-19 topics may also be predictive of future confirmed case counts, though further studies to validate these findings are needed.

Challenges in defining Long COVID: Striking differences across literature, Electronic Health Records, and patient-reported information

Since late 2019, the novel coronavirus SARS-CoV-2 has introduced a wide array of health challenges globally. In addition to a complex acute presentation that can affect multiple organ systems, increasing evidence points to long-term sequelae being common and impactful. The worldwide scientific community is forging ahead to characterize a wide range of outcomes associated with SARS-CoV-2 infection; however the underlying assumptions in these studies have varied so widely that the resulting data are difficult to compareFormal definitions are needed in order to design robust and consistent studies of Long COVID that consistently capture variation in long-term outcomes. Even the condition itself goes by three terms, most widely "Long COVID", but also "COVID-19 syndrome (PACS)" or, "post-acute sequelae of SARS-CoV-2 infection (PASC)". In the present study, we investigate the definitions used in the literature published to date and compare them against data available from electronic health records and patient-reported information collected via surveys. Long COVID holds the potential to produce a second public health crisis on the heels of the pandemic itself. Proactive efforts to identify the characteristics of this heterogeneous condition are imperative for a rigorous scientific effort to investigate and mitigate this threat.