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schengal4/LACE_model
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The LACE Score Calculator is an intuitive Streamlit web application tailored for healthcare professionals to evaluate the risk of hospital readmission or death within 30 days post-discharge. It implements the LACE index scoring system, which takes into account the Length of stay, Acuity of admission, Comorbidities, and Emergency department visits; see https://txhca.org/app/uploads/2015/08/Aug.-2015-LACE-Tool.pdf for more details. Users are provided with two convenient options for data entry: manual input for individual patient assessments or bulk upload via Medicare claims in CSV format, supporting files up to 200MB. Upon navigating to the app, users can start by uploading their Medicare fee-for-service claim file through a simple drag-and-drop interface or by clicking the 'Browse files' button. For those new to the app or interested in a demonstration, there is an option to 'Try an example file,' which showcases the app's capabilities using a pre-loaded dataset. The results are displayed in an organized LACE Score Table, which allows for user-friendly interactions such as sorting by any column header, downloading data directly from the table, and utilizing a search feature for quick access to specific patient data. The table provides a clear display of each patient's LACE score along with associated risk levels and other pertinent details. For evaluations of individual patients, the LACE Index Score Calculator screen presents a straightforward four-step process, guiding users through the assessment of Length of Stay, Acuity of Admission, Comorbidities (with a comprehensive list of conditions), and Emergency Department visits within the past six months. Each section is clearly delineated, with informational tooltips that provide additional guidance on how to score each part accurately. After the data input, the calculated LACE index is immediately presented, along with a risk stratification (e.g., "The patient's LACE index is 8. This suggests that the patient is at moderate risk for hospital readmission."), enabling swift and informed clinical decision-making.

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Use Cases Limitations Evidence Owner's Insight

Hospital Discharge Planning: Assisting healthcare providers in identifying patients at higher risk of readmission for enhanced post-discharge care planning.Clinical Decision Support: Integrating with Electronic Health Records (EHR) to provide clinicians with real-time risk assessments during hospitalization.Quality Improvement Initiatives: Using the LACE score data to inform hospital policies aimed at reducing readmission rates, which can improve patient outcomes and potentially lower costs.Research Applications: Facilitating research studies on patient readmissions and outcomes by providing a standardized measure of readmission risk.

Data Dependent: The accuracy of the LACE score is highly dependent on the quality and completeness of the input data.Static Model: The LACE index is a static model and may not capture the nuances of individual patient circumstances or changes over time. Population Specific: The LACE index was initially validated in certain populations in Ontario, Canada. Although it's now validated in several populations, its performance can vary depending on the demographics or clinical settings.

The LACE Score Calculator calculates patients' LACE indices. The LACE index—an acronym for Length of stay, Acuity of admission, Comorbidities, and Emergency department visits—itself is a predictive model to estimate the risk of post-hospital discharge readmission or death within 30 days. More details of the algorithm can be found at https://txhca.org/app/uploads/2015/08/Aug.-2015-LACE-Tool.pdf. The LACE index is based on "Derivation and validation of an index to predict early death or unplanned readmission after discharge from hospital to the community" (published in 2010 in CMAJ), a prospective cohort study that analyzed data from over 4,800 patients discharged from hospitals in Ontario, Canada. This study used a split-sample design to derive and validate an index (LACE) to predict the risk of death or nonelective readmission within 30 days after discharge. Then, this index was externally validated using administrative data in a random selection of 1,000,000 Ontarians discharged from hospital between 2004 and 2008; in both the internal validation cohort and the external validation group, the LACE index could predict patients' risk of 30-day readmission (C statistic 0.6935 (internal validation cohort) and 0.684 (external validation group)). Subsequent studies confirm the index’s predictive validity across diverse patient populations. The strength of the LACE index lies in its simplicity and the use of readily available hospital data, making it a practical and accessible tool for healthcare providers. By quantifying key factors that influence patient outcomes after discharge, the LACE index provides a systematic approach to risk stratification. This model has been adopted in various healthcare settings globally.

I'm Venkata Chengalvala, the main developer of this app. As an AI consultant for Health Universe, I port high-quality peer-reviewed AI models helpful to clinicians, patients, and/or researchers to Health Universe's platform. I have a Bachelor of Science in Molecular, Cellular, and Developmental Biology (MCDB) and Computer Science from the University of Michigan-Ann Arbor. During my education, I engaged in medical research, co-authoring a literature review on tumor-derived exosomes, cited by 30+ people so far: https://www.sciencedirect.com/science/article/pii/S2211383521001398.

The LACE Score Calculator aims to streamline the process for hospitals to evaluate readmission risks, thus freeing up clinicians to focus on patient care. Looking ahead, I'm excited about enhancing the app with a peer-reviewed AI model that's shown to outperform the traditional LACE index.

Your insights and feedback on this app are greatly appreciated. If you have any thoughts or feedback on this app, feel free to leave a comment below.

Peer reviewed

Warning: This application or model has been peer reviewed, but still may occasionally produce unsafe outputs.


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Venkata Chengalvala

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