Estimating the lengths-of-stay (LoS) of hospitalised COVID-19 patients is key for predicting the hospital beds' demand and planning mitigation strategies, as overwhelming the healthcare systems has critical consequences for disease mortality. However, accurately mapping the time-to-event of hospital outcomes, such as the LoS in the intensive care unit (ICU), requires understanding patient trajectories while adjusting for covariates and observation bias, such as incomplete data. Standard methods, such as the Kaplan-Meier estimator, require prior assumptions that are untenable given current knowledge. Using real-time surveillance data from the first weeks of the COVID-19 epidemic in Galicia (Spain), we aimed to model the time-to-event and event probabilities of patients' hospitalised, without parametric priors and adjusting for individual covariates. We applied a non-parametric mixture cure model and compared its performance in estimating hospital ward (HW)/ICU LoS to the performances of commonly used methods to estimate survival. We showed that the proposed model outperformed standard approaches, providing more accurate ICU and HW LoS estimates. Finally, we applied our model estimates to simulate COVID-19 hospital demand using a Monte Carlo algorithm. We provided evidence that adjusting for sex, generally overlooked in prediction models, together with age is key for accurately forecasting HW and ICU occupancy, as well as discharge or death outcomes.

翻译：估算新冠肺炎住院患者的住院时长对于预测医院床位需求及制定缓解策略至关重要，因为医疗系统超负荷将直接影响疾病死亡率。然而，准确描述住院结局的时间事件特征（如重症监护病房住院时长）需要理解患者病程轨迹，同时需调整协变量并处理观察偏倚（如数据不完整）。标准方法（如Kaplan-Meier估计器）需要基于现有知识难以支持的先验假设。本研究利用西班牙加利西亚地区新冠疫情最初几周的实时监测数据，在不依赖参数先验假设并调整个体协变量的前提下，建立了患者住院时间事件及事件概率模型。我们采用非参数混合治愈模型，将其估算普通病房/重症监护病房住院时长的性能与常用生存分析方法进行对比。结果表明，该模型优于标准方法，可提供更精准的ICU和普通病房住院时长估计。最终，我们运用蒙特卡洛算法基于模型估计值模拟了新冠肺炎住院需求。研究证实，在预测模型中通常被忽略的性别因素与年龄因素共同调整，是精准预测普通病房和ICU占用率、以及出院或死亡结局的关键。