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

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

Prenatal maternal stress was not associated with birthweight or gestational age at birth during COVID-19 restrictions in Australia : the BITTOC longitudinal cohort study

Background: Various forms of prenatal maternal stress (PNMS) have been reported to increase risk for preterm birth and low birthweight. However, the associations between specific components of stress – namely objective hardship and subjective distress - and birth outcomes are not well understood. Aims: Here, we aimed to determine the relationship between birthweight and gestational age at birth and specific prenatal factors (infant gender and COVID-19 pandemic-related objective hardship, subjective distress, change in diet), and to determine whether effects of hardship are moderated by maternal subjective distress, change in diet, or infant gender. Materials and methods: As part of the Birth in the Time of COVID (BITTOC study), women (N = 2285) who delivered in Australia during the pandemic were recruited online between August 2020 and February 2021. We assessed objective hardship and subjective distress related to the COVID pandemic and restrictions, and birth outcomes through questionnaires that were completed at recruitment and two months post-partum. Analyses included hierarchical multiple regressions. Results: No associations between maternal objective hardship or subjective distress and gestational age at birth or birthweight were identified. Lower birthweight was significantly associated with female gender (adjusted β = 0.083, P < 0.001) and with self-reported improvement in maternal diet (adjusted β = 0.059, P = 0.015). Conclusions: In a socioeconomically advantaged sample, neither objective hardship nor subjective distress related to COVID-19 were associated with birth outcomes. Further research is warranted to understand how other individual factors influence susceptibility to PNMS and how these findings are applicable to women with lower socioeconomic status

Méthode globale de prédiction des durées de séjours hospitalières avec intégration des données incrémentales et évolutives

Predicting patient length of stay is an important issue for the organization of care activities in hospitals, especially for beds management the and preparation for patients discharge. Facilitating the organization of hospital activities influences access, quality and efficiency of care. In this thesis, we sought to predict length of stay for all patients in the hospital, at all stages that make up their care pathways, using standardized Medical, Surgical, Obstetric medico-administrative data collected for reimbursement of care. We began by conducting a systematic review of the literature on methods for predicting lengths of stay, in order to better understand data preparation, the different prediction approaches, and how to report the results. We then worked on a data preprocessing method and investigated the ability of embeddings to represent medical concepts in the context of length of stay predictions via a neural network. The ability of the neural network to correctly predict length of stay was rigorously evaluated and compared with a random forest and a logistic regression. This work shows that hospital length of stay can be predicted by a neural network using standardized medical-administrative data available for all patients.Prédire la durée de séjour des patients est un enjeu important pour l'organisation des activités de soin dans les hôpitaux, notamment en termes de gestion des lits et de préparation de la sortie des patients. Faciliter l'organisation des activités de l'hôpital influence l'accès, la qualité et l'efficience des soins. Dans cette thèse, nous avons cherché à prédire la durée de séjour pour tous les patients de l'hôpital, à toutes les étapes qui composent leurs parcours de soins, à l'aide de données médico-administratives standardisées de Médecine, Chirurgie, Obstétrique qui sont collectées pour le remboursement des soins. Nous avons commencé par faire une revue systématique de la littérature sur les méthodes de prédiction des durées de séjours, afin de mieux comprendre la préparation des données, les différentes approches de prédiction et la façon de rapporter les résultats. Nous avons ensuite travaillé sur une méthode de prétraitement des données et déterminé si les embeddings peuvent représenter les concepts médicaux dans le cadre des prédictions de durées de séjours via un réseau de neurones. La capacité du réseau de neurones à correctement prédire la durée de séjour a été évaluée et comparée avec celle d'une forêt aléatoire et d'une régression logistique. Nos travaux montrent que la durée de séjour hospitalière peut être prédite au moyen d'un réseau de neurones avec des données médico-administratives standardisées disponibles pour tous les patients

Méthode globale de prédiction des durées de séjours hospitalières avec intégration des données incrémentales et évolutives

Predicting patient length of stay is an important issue for the organization of care activities in hospitals, especially for beds management the and preparation for patients discharge. Facilitating the organization of hospital activities influences access, quality and efficiency of care. In this thesis, we sought to predict length of stay for all patients in the hospital, at all stages that make up their care pathways, using standardized Medical, Surgical, Obstetric medico-administrative data collected for reimbursement of care. We began by conducting a systematic review of the literature on methods for predicting lengths of stay, in order to better understand data preparation, the different prediction approaches, and how to report the results. We then worked on a data preprocessing method and investigated the ability of embeddings to represent medical concepts in the context of length of stay predictions via a neural network. The ability of the neural network to correctly predict length of stay was rigorously evaluated and compared with a random forest and a logistic regression. This work shows that hospital length of stay can be predicted by a neural network using standardized medical-administrative data available for all patients.Prédire la durée de séjour des patients est un enjeu important pour l'organisation des activités de soin dans les hôpitaux, notamment en termes de gestion des lits et de préparation de la sortie des patients. Faciliter l'organisation des activités de l'hôpital influence l'accès, la qualité et l'efficience des soins. Dans cette thèse, nous avons cherché à prédire la durée de séjour pour tous les patients de l'hôpital, à toutes les étapes qui composent leurs parcours de soins, à l'aide de données médico-administratives standardisées de Médecine, Chirurgie, Obstétrique qui sont collectées pour le remboursement des soins. Nous avons commencé par faire une revue systématique de la littérature sur les méthodes de prédiction des durées de séjours, afin de mieux comprendre la préparation des données, les différentes approches de prédiction et la façon de rapporter les résultats. Nous avons ensuite travaillé sur une méthode de prétraitement des données et déterminé si les embeddings peuvent représenter les concepts médicaux dans le cadre des prédictions de durées de séjours via un réseau de neurones. La capacité du réseau de neurones à correctement prédire la durée de séjour a été évaluée et comparée avec celle d'une forêt aléatoire et d'une régression logistique. Nos travaux montrent que la durée de séjour hospitalière peut être prédite au moyen d'un réseau de neurones avec des données médico-administratives standardisées disponibles pour tous les patients

Global method for predicting the hospital length of stay using incremental and evolutionary data

Prédire la durée de séjour des patients est un enjeu important pour l'organisation des activités de soin dans les hôpitaux, notamment en termes de gestion des lits et de préparation de la sortie des patients. Faciliter l'organisation des activités de l'hôpital influence l'accès, la qualité et l'efficience des soins. Dans cette thèse, nous avons cherché à prédire la durée de séjour pour tous les patients de l'hôpital, à toutes les étapes qui composent leurs parcours de soins, à l'aide de données médico-administratives standardisées de Médecine, Chirurgie, Obstétrique qui sont collectées pour le remboursement des soins. Nous avons commencé par faire une revue systématique de la littérature sur les méthodes de prédiction des durées de séjours, afin de mieux comprendre la préparation des données, les différentes approches de prédiction et la façon de rapporter les résultats. Nous avons ensuite travaillé sur une méthode de prétraitement des données et déterminé si les embeddings peuvent représenter les concepts médicaux dans le cadre des prédictions de durées de séjours via un réseau de neurones. La capacité du réseau de neurones à correctement prédire la durée de séjour a été évaluée et comparée avec celle d'une forêt aléatoire et d'une régression logistique. Nos travaux montrent que la durée de séjour hospitalière peut être prédite au moyen d'un réseau de neurones avec des données médico-administratives standardisées disponibles pour tous les patients.Predicting patient length of stay is an important issue for the organization of care activities in hospitals, especially for beds management the and preparation for patients discharge. Facilitating the organization of hospital activities influences access, quality and efficiency of care. In this thesis, we sought to predict length of stay for all patients in the hospital, at all stages that make up their care pathways, using standardized Medical, Surgical, Obstetric medico-administrative data collected for reimbursement of care. We began by conducting a systematic review of the literature on methods for predicting lengths of stay, in order to better understand data preparation, the different prediction approaches, and how to report the results. We then worked on a data preprocessing method and investigated the ability of embeddings to represent medical concepts in the context of length of stay predictions via a neural network. The ability of the neural network to correctly predict length of stay was rigorously evaluated and compared with a random forest and a logistic regression. This work shows that hospital length of stay can be predicted by a neural network using standardized medical-administrative data available for all patients

Hospital Length of Stay Prediction Methods: A Systematic Review

International audienceObjective: This systematic review sought to establish a picture of length of stay (LOS) prediction methods based on available hospital data and study protocols designed to measure their performance.Materials and Methods: An English literature search was done relative to hospital LOS prediction from 1972 to September 2019 according to the PRISMA guidelines. Articles were retrieved from PubMed, ScienceDirect, and arXiv databases. Information were extracted from the included papers according to a standardized assessment of population setting and study sample, data sources and input variables, LOS prediction methods, validation study design, and performance evaluation metrics.Results: Among 74 selected articles, 98.6% (73/74) used patients’ data to predict LOS; 27.0% (20/74) used temporal data; and 21.6% (16/74) used the data about hospitals. Overall, regressions were the most popular prediction methods (64.9%, 48/74), followed by machine learning (20.3%, 15/74) and deep learning (17.6%, 13/74). Regarding validation design, 35.1% (26/74) did not use a test set, whereas 47.3% (35/74) used a separate test set, and 17.6% (13/74) used cross-validation. The most used performance metrics were R2 (47.3%, 35/74), mean squared (or absolute) error (24.4%, 18/74), and the accuracy (14.9%, 11/74). Over the last decade, machine learning and deep learning methods became more popular (P=0.016), and test sets and cross-validation got more and more used (P=0.014).Conclusions: Methods to predict LOS are more and more elaborate and the assessment of their validity is increasingly rigorous. Reducing heterogeneity in how these methods are used and reported is key to transparency on their performance

Length of Stay Prediction With Standardized Hospital Data From Acute and Emergency Care Using a Deep Neural Network

International audienceObjective: Length of stay (LOS) is an important metric for the organization and scheduling of care activities. This study sought to propose a LOS prediction method based on deep learning using widely available administrative data from acute and emergency care and compare it with other methods.Patients and Methods: All admissions between January 1, 2011 and December 31, 2019, at 6 university hospitals of the Hospices Civils de Lyon metropolis were included, leading to a cohort of 1,140,100 stays of 515,199 patients. Data included demographics, primary and associated diagnoses, medical procedures, the medical unit, the admission type, socio-economic factors, and temporal information. A model based on embeddings and a Feed-Forward Neural Network (FFNN) was developed to provide fine-grained LOS predictions per hospitalization step. Performances were compared with random forest and logistic regression, with the accuracy, Cohen kappa, and a Bland-Altman plot, through a 5-fold cross-validation.Results: The FFNN achieved an accuracy of 0.944 (CI: 0.937, 0.950) and a kappa of 0.943 (CI: 0.935, 0.950). For the same metrics, random forest yielded 0.574 (CI: 0.573, 0.575) and 0.602 (CI: 0.601, 0.603), respectively, and 0.352 (CI: 0.346, 0.358) and 0.414 (CI: 0.408, 0.422) for the logistic regression. The FFNN had a limit of agreement ranging from −2.73 to 2.67, which was better than random forest (−6.72 to 6.83) or logistic regression (−7.60 to 9.20).Conclusion: The FFNN was better at predicting LOS than random forest or logistic regression. Implementing the FFNN model for routine acute care could be useful for improving the quality of patients’ care

Predicting length of stay with administrative data from acute and emergency care: an embedding approach

International audienceHospital beds management is critical for the quality of patient care, while length of inpatient stay is often estimated empirically by physicians or chief nurses of medical wards. Providing an efficient method for forecasting the length of stay (LOS) is expected to improve resources and discharges planning. Predictions should be accurate and work for as many patients as possible, despite their heterogeneous profiles. In this work, a LOS prediction method based on deep learning and embeddings is developed by using generic hospital administrative data from a French national hospital discharge database, as well as emergency care. Data concerned 497 626 stays of 304 931 patients from 6 hospitals in Lyon, France, from 2011 to 2019. Results of a 5-fold cross-validation showed an accuracy of 0.73 and a kappa score of 0.67 for the embeddings method. This outperformed the baseline which used the raw input features directly

Predicting length of stay with administrative data from acute and emergency care: an embedding approach

International audienceHospital beds management is critical for the quality of patient care, while length of inpatient stay is often estimated empirically by physicians or chief nurses of medical wards. Providing an efficient method for forecasting the length of stay (LOS) is expected to improve resources and discharges planning. Predictions should be accurate and work for as many patients as possible, despite their heterogeneous profiles. In this work, a LOS prediction method based on deep learning and embeddings is developed by using generic hospital administrative data from a French national hospital discharge database, as well as emergency care. Data concerned 497 626 stays of 304 931 patients from 6 hospitals in Lyon, France, from 2011 to 2019. Results of a 5-fold cross-validation showed an accuracy of 0.73 and a kappa score of 0.67 for the embeddings method. This outperformed the baseline which used the raw input features directly

Doc2Vec on the PubMed corpus: study of a new approach to generate related articles

PubMed is the biggest and most used bibliographic database worldwide, hosting more than 26M biomedical publications. One of its useful features is the "similar articles" section, allowing the end-user to find scientific articles linked to the consulted document in term of context. The aim of this study is to analyze whether it is possible to replace the statistic model PubMed Related Articles (pmra) with a document embedding method. Doc2Vec algorithm was used to train models allowing to vectorize documents. Six of its parameters were optimised by following a grid-search strategy to train more than 1,900 models. Parameters combination leading to the best accuracy was used to train models on abstracts from the PubMed database. Four evaluations tasks were defined to determine what does or does not influence the proximity between documents for both Doc2Vec and pmra. The two different Doc2Vec architectures have different abilities to link documents about a common context. The terminological indexing, words and stems contents of linked documents are highly similar between pmra and Doc2Vec PV-DBOW architecture. These algorithms are also more likely to bring closer documents having a similar size. In contrary, the manual evaluation shows much better results for the pmra algorithm. While the pmra algorithm links documents by explicitly using terminological indexing in its formula, Doc2Vec does not need a prior indexing. It can infer relations between documents sharing a similar indexing, without any knowledge about them, particularly regarding the PV-DBOW architecture. In contrary, the human evaluation, without any clear agreement between evaluators, implies future studies to better understand this difference between PV-DBOW and pmra algorithm