Dataset Information

COVID-19 and Hospitalization Among Maintenance Dialysis Patients: A Retrospective Cohort Study Using Time-Dependent Modeling.

ABSTRACT:

Rationale & objective

The coronavirus disease 2019 (COVID-19) pandemic has had a profound impact on hospitalizations in general and on dialysis patients in particular. This study modeled the impact of COVID-19 on hospitalizations of dialysis patients in 2020.

Study design

Retrospective cohort study.

Setting & participants

Medicare patients on dialysis in calendar year 2020.

Predictors

COVID-19 status was divided into 4 stages: COVID1 (first 10 days after initial diagnosis), COVID2 (extends until the Post-COVID stage), Post-COVID (after 21 days with no COVID-19 diagnosis), and Late-COVID (begins after a hospitalization with a COVID-19 diagnosis); demographic and clinical characteristics; and dialysis facilities.

Outcome

The sequence of hospitalization events.

Analytical approach

A proportional rate model with a nonparametric baseline rate function of calendar time on the study population.

Results

A total of 509,609 patients were included in the study, 63,521 were observed to have a SARS-CoV-2 infection, 34,375 became Post-COVID, and 1,900 became Late-COVID. Compared with No-COVID, all 4 stages had significantly greater adjusted risks of hospitalizations with relative rates of 18.50 (95% CI, 18.19-18.81) for COVID1, 2.03 (95% CI, 1.99-2.08) for COVID2, 1.37 (95% CI, 1.35-1.40) for Post-COVID, and 2.00 (95% CI, 1.89-2.11) for Late-COVID.

Limitations

For Medicare Advantage patients, we only had inpatient claim information. The analysis was based on data from the year 2020, and the effects may have changed due to vaccinations, new treatments, and new variants. The COVID-19 effects may be somewhat overestimated due to missing information on patients with few or no symptoms and possible delay in COVID-19 diagnosis.

Conclusions

We discovered a marked time dependence in the effect of COVID-19 on hospitalization of dialysis patients, beginning with an extremely high risk for a relatively short period, with more moderate but continuing elevated risks later, and never returning to the No-COVID level.

SUBMITTER: Ding X

PROVIDER: S-EPMC9398821 | biostudies-literature | 2022 Nov

REPOSITORIES: biostudies-literature

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Publications

COVID-19 and Hospitalization Among Maintenance Dialysis Patients: A Retrospective Cohort Study Using Time-Dependent Modeling.

Ding Xuemei X Wang Xi X Gremel Garrett W GW He Kevin K Kang Jian J Messana Joseph M JM Dahlerus Claudia C Wu Wenbo W Hirth Richard A RA Kalbfleisch John D JD

Kidney medicine 20220824 11

<h4>Rationale & objective</h4>The coronavirus disease 2019 (COVID-19) pandemic has had a profound impact on hospitalizations in general and on dialysis patients in particular. This study modeled the impact of COVID-19 on hospitalizations of dialysis patients in 2020.<h4>Study design</h4>Retrospective cohort study.<h4>Setting & participants</h4>Medicare patients on dialysis in calendar year 2020.<h4>Predictors</h4>COVID-19 status was divided into 4 stages: COVID1 (first 10 days after initial diag ...[more]

PMID: 36035616

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Project description:BackgroundRespiratory pathogens, including SARS-CoV-2, can cause pulmonary structural damage and physiologic impairment, which may increase the risk of subsequent lower respiratory tract infections (LRTI). Prior hospitalization for any reason is a risk factor for LRTI, but data on the risk of subsequent new-onset LRTI following hospitalization for COVID-19 LRTI or non-COVID-19 LRTI are needed to inform strategies for immunizations targeting respiratory pathogens.MethodsWe conducted a retrospective cohort study at Kaiser Permanente Southern California (KPSC) among adults hospitalized from 3/1/2020 to 5/31/2022, excluding labor and delivery. We categorized individuals into 3 mutually exclusive baseline exposure groups: those hospitalized for COVID-19 LRTI, those hospitalized for non-COVID-19 LRTI, and those hospitalized for all other causes without LRTI or COVID-19 ("non-LRTI"). Following hospital discharge, patients were followed up for new-onset LRTI, beginning 30 antibiotic-free days after hospital discharge until 8/31/2022. We used multivariable cause-specific Cox regression with time-varying covariates to estimate hazard ratios (HR) of new-onset LRTI comparing those hospitalized for COVID-19 LRTI or non-COVID-19 LRTI to those hospitalized for non-LRTI, adjusting for demographic and clinical characteristics.ResultsThe study included 22,417 individuals hospitalized for COVID-19 LRTI, 12,795 individuals hospitalized for non-COVID-19 LRTI, and 176,788 individuals hospitalized for non-LRTI. Individuals hospitalized for non-COVID-19 LRTI were older and had more comorbidities than those hospitalized for COVID-19 LRTI or non-LRTI. Incidence rates per 1,000 person-years (95% CI) of new-onset LRTI were 52.5 (51.4-53.6) among individuals hospitalized for COVID-19 LRTI, 253.5 (243.7-263.6) among those hospitalized for non-COVID-19 LRTI, and 52.5 (51.4-53.6) among those hospitalized for non-LRTI. The adjusted hazard of new-onset LRTI during follow-up was 20% higher among individuals hospitalized for COVID-19 LRTI (HR 1.20 [95% CI: 1.12-1.28]) and 301% higher among individuals hospitalized for non-COVID-19 LRTI (HR 3.01 [95% CI: 2.87-3.15]) compared to those hospitalized for non-LRTI.ConclusionThe risk of new-onset LRTI following hospital discharge was high, particularly among those hospitalized for non-COVID-19 LRTI, but also for COVID-19 LRTI. These data suggest that immunizations targeting respiratory pathogens, including COVID-19, should be considered for adults hospitalized for LRTI prior to hospital discharge.

Project description:BackgroundBoth dialysis facilities and hospitals are accountable for 30-day hospital readmissions among U.S. hemodialysis patients. We examined the association of post-hospitalization processes of care at hemodialysis facilities with pulmonary edema-related and other readmissions.MethodsIn a retrospective cohort comprised of electronic medical record (EMR) data linked with national registry data, we identified unique patient index admissions (n = 1056; 2/1/10-7/31/15) that were followed by ≥3 in-center hemodialysis sessions within 10 days, among patients treated at 19 Southeastern dialysis facilities. Indicators of processes of care were defined as present vs. absent in the dialysis facility EMR. Readmissions were defined as admissions within 30 days of the index discharge; pulmonary edema-related vs. other readmissions defined by discharge codes for pulmonary edema, fluid overload, and/or congestive heart failure. Multinomial logistic regression to estimate odds ratios (ORs) for pulmonary edema-related and other vs. no readmissions.ResultsOverall, 17.7% of patients were readmitted, and 8.0% had pulmonary edema-related readmissions (44.9% of all readmissions). Documentation of the index admission (OR = 2.03, 95% CI 1.07-3.85), congestive heart failure (OR = 1.87, 95% CI 1.07-3.27), and home medications stopped (OR = 1.81, 95% CI 1.08-3.05) or changed (OR = 1.69, 95% CI 1.06-2.70) in the EMR post-hospitalization were all associated with higher risk of pulmonary edema-related vs. no readmission; lower post-dialysis weight (by ≥0.5 kg) after vs. before hospitalization was associated with 40% lower risk (OR = 0.60, 95% CI 0.37-0.96).ConclusionsOur results suggest that some interventions performed at the dialysis facility in the post-hospitalization period may be associated with reduced readmission risk, while others may provide a potential existing means of identifying patients at higher risk for readmissions, to whom such interventions could be efficiently targeted.

Dataset Information

COVID-19 and Hospitalization Among Maintenance Dialysis Patients: A Retrospective Cohort Study Using Time-Dependent Modeling.

Rationale & objective

Study design

Setting & participants

Predictors

Outcome

Analytical approach

Results

Limitations

Conclusions

Publications

COVID-19 and Hospitalization Among Maintenance Dialysis Patients: A Retrospective Cohort Study Using Time-Dependent Modeling.

Similar Datasets

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