Machine learning models featuring somatic and mental comorbidities for prolonged length-of-stay in a maximum care university hospital [code] - Stahl-Toyota, Sophia; Dönnhoff, Ivo; Nagy, Ede; Hochlehnert, Achim; Bönsel, Stefan; Unger, Inga; Szendrödi, Julia; Frey, Norbert; Michl, Patrick; Müller-Tidow, Carsten; Jäger, Dirk; Friederich, Hans-Christoph; et al. (Universität, 2025)
Abstract Background: Knowledge about the influencing factors on hospital in-patient length-of-stay is integral for optimizing care and resource planning. Existing studies on prolonged length-of-stay prediction choose a precise figure as threshold for the number of days that classifies the length-of-stay as prolonged and base the analysis on either a large and diverse sample or a very specific cohort. Most studies take somatic comorbidities into account, while only a subset incorporates mental comorbidities, with varying definitions of the composition of comorbidity subgroups. Objectives: (I) After which timeframe does the number of days of inpatient treatment indicate a prolonged length-of-stay if the threshold for outliers is computed department-wise in a maximum care internal medicine university hospital? (II) How accurate can machine learning models predict prolonged length-of-stay in internal medicine patients? (III) Which mental and somatic comorbidities have the strongest influence on length-of-stay prediction? Methods: From six internal medicine departments at the German University Hospital in Heidelberg, a total of N=28,536 cases treated in the years 2017 to 2019 comprised the study population for which a department-level threshold for prolonged length-of-stay was computed. For each of the six departments, four machine learning models were built that were based on the prolonged length-of-stay classification on variables derived from demographics and mental as well as somatic comorbidities. Results: Length-of-stay was classified as prolonged if the number of days at the hospital equaled or exceeded 9 (Cardiology), 10 (General and Psychosomatics, Gastroenterology, Medical Oncology), 11 (Endocrinology) or 26 (Hematology). With Area under the Receiver Operator Curve (AUROC)=0.89 the random forest for the Department of Hematology had the highest predictive power, the random forest for the Department of General and Psychosomatic with AUROC=0.72 the lowest. The variables with strongest influence on the prediction comprised the number of somatic comorbidities, the age at diagnosis, mental and somatic comorbidity subgroups. Among the mental comorbidities, stress-related adjustment disorder was the most prominent factor. Conclusions: Consideration of department-level factors is recommended for prolonged length-of-stay prediction models. Mental as well as somatic comorbidities were among the most relevant factors for the prediction of prolonged length-of-stay and require adequate treatment and reimbursement opportunities.
Gesehen am 07.08.2025
Keywords: Forschungsdaten; Datenbank