A latent transfer learning method for estimating hospital-specific post-acute healthcare demands following SARS-CoV-2 infection

Abstract

The long-term complications of COVID-19, known as the post-acute sequelae of SARS-CoV-2 infection (PASC), significantly burden healthcare resources. Quantifying the demand for post-acute healthcare is essential for understanding patients' needs and optimizing the allocation of valuable medical resources for disease management. Driven by this need, we developed a heterogeneous latent transfer learning framework (Latent-TL) to generate critical insights for individual health systems in a distributed research network. Latent-TL enhances learning in a specific health system by borrowing information from all other health systems in the network in a data-driven fashion. By identifying subpopulations with varying healthcare needs, our Latent-TL framework can provide more effective guidance for decision-making. Applying Latent-TL to electronic health record (EHR) data from eight health systems in PEDSnet, a national learning health system in the US, revealed four distinct patient subpopulations with heterogeneous post-acute healthcare demands following COVID-19 infections, varying across subpopulations and hospitals.

Keywords: COVID-19; Long COVID; electronic health records; healthcare utilization; learning health system; real-world data; transfer learning.

Figure 1

Overview of study design, cohort attrition, and the heterogeneous Latent-TL pipeline The figure delineates the three core stages to implement the latent transfer learning (Latent-TL) pipeline: (1) identification of latent patient subpopulations characterized by specific multimorbidity patterns based on data from multiple health systems, (2) causal estimation tailored to the patient population in the target hospital, and (3) adaptive integration across hospitals for enhanced estimation.

Figure 2

Distribution of demographic attributes including age, gender, race or ethnicity, and obesity prevalence among patients from each of the eight participating health systems in the PEDSnet network The eight hospitals are indexed from A to H. The height of the bars indicates the prevalence of each demographic variable within each hospital. The outermost circle corresponds to a prevalence of 0.6, with inner circles indicating lower prevalence levels in increments of 0.2. This visualization reveals variations in ethnicity and obesity metrics across different health systems, highlighting the heterogeneity of patient demographics between hospitals.

Figure 3

Collaborative identification of subpopulations based on underlying chronic conditions through MLCA approach Top: a heatmap detailing the four discerned subpopulations (or latent classes). Columns symbolize individual subpopulations, while rows indicate the prevalence of specific chronic condition clusters. The heatmap emphasizes the 50 most prevalent chronic conditions. Each subpopulation is characterized by its unique distribution of chronic condition cluster incidences. Bottom: pie charts illustrating the overall prevalence of each subpopulation and its respective prevalence within individual hospitals.

Figure 4

Estimation of subpopulation-specific impacts of COVID-19 infection on post-acute inpatient visits at hospital A (target hospital) using Latent-TL pipeline The first column illustrates effect sizes estimated by applying standard causal inference within each hospital. The second column shows effect sizes after standardizing the source samples via a weighting mechanism in alignment with the patient characteristics in the target hospital. The final column showcases the causal effects by incorporating the source hospital in a cumulative manner, highlighting the efficiency gains by involving data from source hospitals cumulatively using the Latent-TL pipeline. The error bars represent 95% confidence intervals of estimates. The estimates, marked with the star, have the highest estimation efficiency and are showcased in Figure 6.

Figure 5

Hospital contributions in estimating the subpopulation-specific effect of COVID-19 infection on post-acute inpatient visits in hospital A Contribution magnitudes are determined based on the similarity of COVID-19 effects across different hospitals.

Figure 6

Hospital- and subpopulation-specific COVID-19 effects on post-acute inpatient and ED visits in eight health systems from PEDSnet Hospital- and subpopulation-specific COVID-19 effects on post-acute inpatient (A) and ED (B) visits in eight health systems from PEDSnet. The error bars represent 95% confidence intervals of estimates. The estimates concerning inpatient visits at hospital A, marked with a star, serve as illustrative examples in Figure 4.

Figure 7

Distribution of patient subpopulation and corresponding healthcare utilization with no COVID-19 infection and healthcare utilization during post-acute phase of COVID-19 for four identified subpopulations This analysis involves estimating healthcare utilization in two hypothetical scenarios where all patients were presumed to be either non-infected or infected with COVID-19.

Figure 8

Funnel plot of traditional and calibrated significance testing (A)–(H) corresponds to the results from hospital A through H, respectively. Areas below the dashed line indicate p < 0.05 based on traditional p value calculations. Estimates in orange areas have p < 0.05 after calibrating the empirical null distribution. Blue dots indicate estimates corresponding to negative control variables. The overall coverage of the null hypothesis changed from 70.9% to 91.1% after calibration.