Overview

Author

Affiliation

Vagish Hemmige

Montefiore Medical Center/ Albert Einstein College of Medicine

Clinical background

Cryptococcosis in kidney transplant recipients is uncommon but potentially catastrophic. When transmission occurs through the donor, it often presents early after transplant, may involve the central nervous system, and is associated with substantial morbidity and mortality despite treatment.

The costs of cryptococcus after solid organ transplant are unclear. Although studies have quantified the cost of a single hospitalization for cryptococcus, no study to date has estimated the total excess cost of care due to cryptococcus.

What this analysis does

This project uses the United States Renal Data System to analyze the impact of cryptococcus on costs.

Methods

We selected cryptococcus cases based on the following inclusion criteria:

Age 18 or greater
Prior kidney transplant
ICD9 or ICD10 codes for cryptococcus (2 outpatient or 1 inpatient)
One year of Medicare primary coverage lead-in

Controls were matched 2:1 to cases based on risk-set sampling on the following:

Time since transplant
Diabetes
HIV/AIDS
Age within 10 years
Sampling date for control within 3 years of sampling date for case

Institutional claims data were abstracted as well as Physician/Supplier line-item data.

Results

Pending

Site navigation

Setup: Defines global paths, data sources, cohort inclusion criteria, and analysis-wide constants.
Functions: Reusable helper functions for cohort construction, matching, costing, and modeling.
Create cohort: Constructs the initial time-varying cohort of kidney transplant recipients, defining cohort entry, follow-up structure, and case/control eligibility prior to matching.
Execute matching: Implements risk-set–based greedy matching without replacement to construct the analytic cohort.
Post-match processing: Derives analytic variables, time-aligned cost windows, and follow-up structure after matching.
Modeling: Fits prespecified cost and outcome models using the matched cohort.
Tables: Summary tables and regression outputs generated from the final models.
Figures:Visualizations of costs, risks, and model-based estimates.
About: methods, assumptions, and disclosures

Reproducibility

All analyses presented on this website are fully reproducible.

The complete source code used to perform the full analysis and generate the tables and figures is publicly available at this GitHub repo. The R script R/main.R will run the R subscripts needed for this analysis in the order they are to be run. Model assumptions, parameter values, and analytic workflows are documented in the repository to facilitate transparency and reuse.

Abstracts/manuscripts

This analysis has yet to be published to date.

--- title: "Overview" format: html --- ## Clinical background [Cryptococcosis in kidney transplant recipients](https://academic.oup.com/ofid/article/12/5/ofaf263/8121609) is uncommon but potentially catastrophic. When [transmission occurs through the donor](https://pmc.ncbi.nlm.nih.gov/articles/PMC8096655/), it often presents early after transplant, may involve the central nervous system, and is associated with substantial morbidity and mortality despite treatment. The costs of cryptococcus after solid organ transplant are unclear. Although studies have quantified the cost of a single hospitalization for cryptococcus, no study to date has estimated the total excess cost of care due to cryptococcus. ## What this analysis does This project uses the [**United States Renal Data System**](https://www.niddk.nih.gov/about-niddk/strategic-plans-reports/usrds) to analyze the impact of cryptococcus on costs. ## Methods We selected cryptococcus cases based on the following inclusion criteria: - Age 18 or greater - Prior kidney transplant - ICD9 or ICD10 codes for cryptococcus (2 outpatient or 1 inpatient) - One year of Medicare primary coverage lead-in Controls were matched 2:1 to cases based on risk-set sampling on the following: - Time since transplant - Diabetes - HIV/AIDS - Age within 10 years - Sampling date for control within 3 years of sampling date for case Institutional claims data were abstracted as well as Physician/Supplier line-item data. ## Results Pending ## Site navigation - [**Setup**](quarto/setup.qmd): Defines global paths, data sources, cohort inclusion criteria, and analysis-wide constants. - [**Functions**](quarto/functions.qmd): Reusable helper functions for cohort construction, matching, costing, and modeling. - [**Create cohort**](quarto/create_cohort.qmd): Constructs the initial time-varying cohort of kidney transplant recipients, defining cohort entry, follow-up structure, and case/control eligibility prior to matching. - [**Execute matching**](quarto/execute_matching.qmd): Implements risk-set–based greedy matching without replacement to construct the analytic cohort. - [**Post-match processing**](quarto/postmatch_processing.qmd): Derives analytic variables, time-aligned cost windows, and follow-up structure after matching. - [**Modeling**](quarto/modeling.qmd): Fits prespecified cost and outcome models using the matched cohort. - [**Tables**](quarto/tables.qmd): Summary tables and regression outputs generated from the final models. - [**Figures**](quarto/figures.qmd):Visualizations of costs, risks, and model-based estimates. - [**About**](quarto/about.qmd): methods, assumptions, and disclosures ### Reproducibility All analyses presented on this website are fully reproducible. The complete source code used to perform the full analysis and generate the tables and figures is publicly available at this [GitHub repo](https://github.com/VagishHemmige/Cryptococcus-cost-analysis). The R script `R/main.R` will run the R subscripts needed for this analysis in the order they are to be run. Model assumptions, parameter values, and analytic workflows are documented in the repository to facilitate transparency and reuse. ## Abstracts/manuscripts This analysis has yet to be published to date.