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Background <p>Postoperative symptoms in lung cancer patients are complex and dynamic, yet recovery is highly heterogeneous. Traditional analyses often fail to capture individual recovery trajectories, limiting the ability to provide personalized care. This study aimed to identify distinct postoperative symptom trajectories and their clinical predictors using a person-centered approach.</p> Methods <p>We conducted a prospective longitudinal study with 394 patients undergoing uniportal video-assisted thoracoscopic surgery (uniportal VATS) for early-stage non-small cell lung cancer. Patient-reported symptoms were collected at 1, 7, 14, and 30&#xa0;days postoperatively. Latent Profile Analysis (LPA) was used to identify distinct symptom profiles, and Latent Transition Analysis (LTA) modeled the transitions between these profiles over time. Multinomial logistic regression was used to identify predictors of these transitions.</p> Results <p>LPA identified two distinct recovery profiles: a “Rapid Recovery” group (C1) and a “High-Symptom, Slow Recovery” group (C2). The first postoperative week was a critical window, with 73.0% of patients in the High-Symptom,&#xa0;Slow Recovery group transitioning to the Rapid Recovery group. This transition rate slowed significantly in subsequent weeks. A higher ASA classification, use of a thicker chest tube, and extensive lymph node dissection predicted a slower recovery. Conversely, better pulmonary function (FEV1%, MVV%) facilitated a faster transition, while postoperative complications were associated with a negative trajectory shift.</p> Conclusions <p>Postoperative recovery in lung cancer patients follows predictable, heterogeneous trajectories. This person-centered approach enables the early identification of high-risk patients based on preoperative and surgical factors. Understanding these distinct pathways allows for a shift from a one-size-fits-all model to staged, personalized interventions designed to optimize symptom management and enhance patient recovery.</p>

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Postoperative symptom trajectories in early-stage lung cancer: a latent transition analysis

  • 期刊论文

  • 发表日期：

    2026年1月12日

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  • Hang Yi,
  • Qian Hong,
  • Yan Wang,
  • Yinyan Gao,
  • Haiyue Sun,
  • Guochao Zhang,
  • Fengyan Ma

摘要

Background

Postoperative symptoms in lung cancer patients are complex and dynamic, yet recovery is highly heterogeneous. Traditional analyses often fail to capture individual recovery trajectories, limiting the ability to provide personalized care. This study aimed to identify distinct postoperative symptom trajectories and their clinical predictors using a person-centered approach.

Methods

We conducted a prospective longitudinal study with 394 patients undergoing uniportal video-assisted thoracoscopic surgery (uniportal VATS) for early-stage non-small cell lung cancer. Patient-reported symptoms were collected at 1, 7, 14, and 30 days postoperatively. Latent Profile Analysis (LPA) was used to identify distinct symptom profiles, and Latent Transition Analysis (LTA) modeled the transitions between these profiles over time. Multinomial logistic regression was used to identify predictors of these transitions.

Results

LPA identified two distinct recovery profiles: a “Rapid Recovery” group (C1) and a “High-Symptom, Slow Recovery” group (C2). The first postoperative week was a critical window, with 73.0% of patients in the High-Symptom, Slow Recovery group transitioning to the Rapid Recovery group. This transition rate slowed significantly in subsequent weeks. A higher ASA classification, use of a thicker chest tube, and extensive lymph node dissection predicted a slower recovery. Conversely, better pulmonary function (FEV1%, MVV%) facilitated a faster transition, while postoperative complications were associated with a negative trajectory shift.

Conclusions

Postoperative recovery in lung cancer patients follows predictable, heterogeneous trajectories. This person-centered approach enables the early identification of high-risk patients based on preoperative and surgical factors. Understanding these distinct pathways allows for a shift from a one-size-fits-all model to staged, personalized interventions designed to optimize symptom management and enhance patient recovery.