Hospital length of stay prediction tools for all hospital admissions and general medicine populations: systematic review and meta-analysis

Abstract

BackgroundUnwarranted extended length of stay (LOS) increases the risk of hospital-acquired complications, morbidity, and all-cause mortality and needs to be recognized and addressed proactively.ObjectiveThis systematic review aimed to identify validated prediction variables and methods used in tools that predict the risk of prolonged LOS in all hospital admissions and specifically General Medicine (GenMed) admissions.MethodLOS prediction tools published since 2010 were identified in five major research databases. The main outcomes were model performance metrics, prediction variables, and level of validation. Meta-analysis was completed for validated models. The risk of bias was assessed using the PROBAST checklist.ResultsOverall, 25 all admission studies and 14 GenMed studies were identified. Statistical and machine learning methods were used almost equally in both groups. Calibration metrics were reported infrequently, with only 2 of 39 studies performing external validation. Meta-analysis of all admissions validation studies revealed a 95% prediction interval for theta of 0.596 to 0.798 for the area under the curve. Important predictor categories were co-morbidity diagnoses and illness severity risk scores, demographics, and admission characteristics. Overall study quality was deemed low due to poor data processing and analysis reporting.ConclusionTo the best of our knowledge, this is the first systematic review assessing the quality of risk prediction models for hospital LOS in GenMed and all admissions groups. Notably, both machine learning and statistical modeling demonstrated good predictive performance, but models were infrequently externally validated and had poor overall study quality. Moving forward, a focus on quality methods by the adoption of existing guidelines and external validation is needed before clinical application.Systematic review registrationhttps://www.crd.york.ac.uk/PROSPERO/, identifier: CRD42021272198.

Is alcohol and psychoactive medication use associated with excess hospital length-of-stay and admission frequency? A cross-sectional, observational study

“…Hospital length-of-stay and admission frequency are ubiquitous metrics in biomedical research, functioning as indicators for disease burden and health care expenditures [ 1 – 5 ].There is considerable interest in identifying factors related to either prolonged or frequent hospitalization [ 6 , 7 ], with subsequent implementation in predictive models and risk stratification tools. Although several dimensions have been assessed, the utility of attributes other than age and co-morbidity ranges between contextual and ambiguous [ 8 , 9 ]. Furthermore, while alcohol consumption and the use of psychoactive prescription medication is frequently encountered in clinical practice, data regarding their interaction with length-of-stay and admission frequency is sparse.…”

Length of hospital stay for liver cancer patients: risk factors and predictive model construction

“…Even among patients with similar severity of illness, the duration of hospitalization can serve as an indicator of the overall quality of diagnosis, treatment and care provided (9). Presently, research on predicting hospitalization risks at the global level remains limited, with a particularly paucity of studies in this domain in China (10,11). Moreover, the existing studies have revealed signi cant heterogeneity in various aspects, such as social structure, medical policies and levels of diagnosis and treatment.…”