United States

Introduction:
In the context of patient safety in hospitals, a significant number of adverse events are identified, with a considerable portion being preventable. Timely detection of potential clinical deterioration events (CDEs) is crucial for mitigating or preventing adverse outcomes. The availability of electronic health records (EHRs) has facilitated the implementation of Artificial Intelligence (AI) algorithms known as &quot;early warning systems&quot; (EWS) for early detection. For an EWS to effectively improve outcomes, it must accurately predict adverse outcomes, do so ahead of clinicians and provide sufficient lead time for intervention. In this study, we evaluated the performance of a widely used EWS, the Epic Deterioration Index (DI), in terms of its ability to provide early warnings before clinicians and its lead time for intervention.

Methods:
Our study was conducted at a large academic medical center. The DI operated in the background, without direct visibility to frontline clinicians. This allowed us to compare the performance of unaided clinicians with the DI concurrently. We analyzed inpatient admissions from 03/01/2022 to 08/31/2022. Out of 21,880 admissions, 18,431 were excluded based on non-CDE hospital course, and 2,813 were excluded as ICU transfers. From the remaining 636 patients, we selected those who experienced a rapid response call or a cardiopulmonary arrest and determined the occurrence of definite or likely CDEs based on established criteria. We retrospectively examined the 72-hour period preceding the CDE to identify the time when clinicians initially recognized the potential clinical deterioration through order and documentation reviews. The lead time for unaided clinicians was calculated based on the first instance of recognition. The DI, running invisibly in the background, assigned patients to low, moderate, or high-risk categories for experiencing a CDE. We identified the first transition from low to moderate/high risk that persisted for at least 1 hour as a signal for potential CDE and used it to calculate the lead time provided by the DI. Comparative analyses were conducted using chi-square and independent t-test calculations.

Results:
Out of the 100 patients who experienced definite or likely CDEs, 81 cases were recognized by either clinicians or DI. The DI identified a CDE in 37 cases, while unaided clinicians detected potential CDEs in 57 cases (χ2 = 8.0, p &lt; 0.01). On average, clinicians had a lead time of 9.13 hours, while the DI provided a lead time of 4.53 hours (p &lt; 0.01). Thus, clinicians detected potential CDEs, on average, 4.53 hours earlier (95% CI [1.44, 7.76]).

Conclusion:
Our findings indicate that clinical judgment has not been entirely superseded by commonly used AI. Unaided clinicians detected potential CDEs more frequently and had significantly longer lead times compared to the DI. However, a notable minority of cases demonstrated that the DI outperformed clinicians in terms of speed. Therefore, while AI should not replace human efforts, it can be a valuable adjunct if appropriately implemented.

2023 AMA Research Challenge

The Race to Save Lives: A Comparative Analysis of Lead Time for Detecting Clinical Deterioration between Clinicians and Artificial Intelligence

Introduction:
In the context of patient safety in hospitals, a significant number of adverse events are identified, with a considerable portion being preventable. Timely detection of potential clinical deterioration events (CDEs) is crucial for mitigating or preventing adverse outcomes. The availability of electronic health records (EHRs) has facilitated the implementation of Artificial Intelligence (AI) algorithms known as "early warning systems" (EWS) for early detection. For an EWS to effectively improve outcomes, it must accurately predict adverse outcomes, do so ahead of clinicians and provide sufficient lead time for intervention. In this study, we evaluated the performance of a widely used EWS, the Epic Deterioration Index (DI), in terms of its ability to provide early warnings before clinicians and its lead time for intervention.

Methods:
Our study was conducted at a large academic medical center. The DI operated in the background, without direct visibility to frontline clinicians. This allowed us to compare the performance of unaided clinicians with the DI concurrently. We analyzed inpatient admissions from 03/01/2022 to 08/31/2022. Out of 21,880 admissions, 18,431 were excluded based on non-CDE hospital course, and 2,813 were excluded as ICU transfers. From the remaining 636 patients, we selected those who experienced a rapid response call or a cardiopulmonary arrest and determined the occurrence of definite or likely CDEs based on established criteria. We retrospectively examined the 72-hour period preceding the CDE to identify the time when clinicians initially recognized the potential clinical deterioration through order and documentation reviews. The lead time for unaided clinicians was calculated based on the first instance of recognition. The DI, running invisibly in the background, assigned patients to low, moderate, or high-risk categories for experiencing a CDE. We identified the first transition from low to moderate/high risk that persisted for at least 1 hour as a signal for potential CDE and used it to calculate the lead time provided by the DI. Comparative analyses were conducted using chi-square and independent t-test calculations.

Results:
Out of the 100 patients who experienced definite or likely CDEs, 81 cases were recognized by either clinicians or DI. The DI identified a CDE in 37 cases, while unaided clinicians detected potential CDEs in 57 cases (χ2 = 8.0, p < 0.01). On average, clinicians had a lead time of 9.13 hours, while the DI provided a lead time of 4.53 hours (p < 0.01). Thus, clinicians detected potential CDEs, on average, 4.53 hours earlier (95% CI [1.44, 7.76]).

Conclusion:
Our findings indicate that clinical judgment has not been entirely superseded by commonly used AI. Unaided clinicians detected potential CDEs more frequently and had significantly longer lead times compared to the DI. However, a notable minority of cases demonstrated that the DI outperformed clinicians in terms of speed. Therefore, while AI should not replace human efforts, it can be a valuable adjunct if appropriately implemented.

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Background
Dietary recall assessments are important clinical tools used by Registered Dietitians (RDs) for assessing eating habits, malnutrition and obesity risk, and health status. Current methods of dietary recall assessment pose barriers to accurately assessing the nutritional intake of older adults due to age-related increases in risk for cognitive decline and more complex health histories. Voice-assistant systems (VAS) are emerging technologies that have been shown to improve the health of older adults. Our study explored the challenges older adults face with dietary recall and whether VAS integration could improve current dietary recall methods from the perspective of RDs.

Methods
This qualitative study utilized semi-structured interviews and demographic questionnaires. We included participants that actively practiced as licensed RDs and were ≥ 22 years of age (n=20). Interviews were conducted over Zoom. RD perspectives were recorded through audio recordings that were professionally transcribed. Transcriptions were inductively analyzed and codified through Dedoose to generate common themes. Qualitative themes included significance of current methods of dietary recall, barriers and facilitators of current methods of dietary recall, and barriers and facilitators of VAS dietary recall.

Results
Of the 20 interviewees, ages ranged from 22-65+ years, of which 9 (45%) were 22-34, 8 (40%) were 35-64, and 3 (15%) were ≥ 65 years. Most of the RDs identified as being female (95%), white (85%), and not of Hispanic, Latino/a/x, or Spanish origin (90%). RDs believed the implementation of VAS had the potential to improve dietary recall assessments of older adults. RDs reported challenges of current dietary recall methods among older adults, such as memory and hearing (65% of participants) and properly reporting serving sizes (45% of participants). RDs reported that VAS dietary recall may be beneficial due to convenience of real-time data collection (80% of participants), more efficient use of time during clinical visits (65% of participants), and ease of accurately reporting meals (85% of participants). However, RDs reported low technology literacy in older adults as a barrier to VAS implementation (70% of participants).

Conclusion
Our qualitative study provides a framework on how VAS can better meet the needs of both older adults and clinical RDs in managing and assessing dietary intake. Future VAS technologies can improve dietary recall methods by incorporating probing cues to enhance real- time data collection, providing clinicians accessibility to patient responses, and including questions that enable collection of data points related to food intake, such as accurate intake times and food-related symptoms.

Understanding the Feasibility of Voice-assistant Systems to Advance Dietary Recall Methods

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**Abstract:**



Association of Strict Versus Lenient Cholesterol Lowering with Cardiac Outcomes, Diabetes Progression and Complications, and Mortality in Patients with Diabetes Treated with Statins: Is Less More?

Background: Accurately predicting the healing time of wounds is a critical aspect of wound care. Developing models that can predict when a wound will heal based on different parameters can provide the healthcare provider and the patient with valuable information. This knowledge can help tailor personalized treatment plans, and optimize resource allocation, thus enhancing patient outcomes. The current project aims to create a predictive model capable of providing precise estimates of wound healing time. 

Methods: A retrospective analysis of 1061 healed wounds was performed using Tissue Analytics (TA). The wounds were stratified into one of six wound etiologies: diabetic, arterial, venous, pressure, traumatic and idiopathic. Different metrics such as total area, length, width and perimeter of the wound, red tissue, black tissue, yellow tissue, other tissue and pink tissue percentages were extrapolated. The number of days in treatment was calculated by subtracting the first presenting day by the day the wound healed. The above information was compiled into a CSV file and Google Colaboratory was employed to generate different regression models using Pycaret. Pycaret automatically tests different machine learning regression models and their efficacy in predicting wound heal time in days.

Results: 20 different machine learning regression models were used and compared to analyze the data and make predictions. Of the 20 different machine learning models, the Random Forest Regressor (RF) had the lowest mean squared error of 1109.46 and root mean squared error of 33.25 days. Extra Trees Regressor (ET) model had the lowest mean absolute error of 26.81 days and mean absolute percentage of 35.51%. The RF model also had the best R-squared of 0.12. A feature importance plot was also generated, which showed that red tissue percentage and etiology were the greatest predictors of wound heal time.

Conclusion: The results showed a high rate of error and low accuracy as indicated by the relatively high MSE and RMSE as well as a low R-sqared for almost all models. Extra metrics generated by Pycaret showed the presence of overfitting, meaning that the models memorized the different numbers to make predictions instead of evaluating the different features. Prior research has shown that wound perimeter and wound area are more likely to predict heal time; however, the data structure could force the the model to determine that etiology and red tissue is best predictor of wound heal time. The current study is limited by the number of wounds as well as other variables that can affect wound heal time such as compliance; however, it can be improved by increasing the number of wounds to train the model.

Leveraging Machine Learning Algorithms to Predict Wound Heal Time

Background: In the United States, hemodialysis is the most common treatment for kidney failure despite the significant health advantages of kidney transplantation. This represents a significant health disparity. The hemodialysis clinic is unique because patients receive therapy in a group setting allowing the patients to form social networks. Patient social networks within hemodialysis facilities may spread kidney transplant (KT) attitudes and behaviors. For the first step in a KT network intervention, we mapped two geographically and demographically different hemodialysis facilities’ social networks to identify the most influential patients as measured by in-degree centrality.  

Methods: We surveyed hemodialysis patients’ social networks in two geographically and demographically different hemodialysis facilities. Statistical and social network analysis were performed in R. We first compared demographic and structural network differences between the two facilities. A multivariable exponential random graph model estimated the associations between in-degree centrality and demographic variables and transplant attitudes/knowledge while controlling for network structure and homophily (in which people form relationships with similar people).  

Results: Participants differed between Facility 1 (N=71) and Facility 2 (N=40) by race (91% Black vs 40%, p<0.001), positive KT attitude (70% vs 45%, p=0.02), and knowing more successful KT recipients (2.5±4.4 vs 0.8±1.1, p=0.002). In-degree centrality was higher at Facility 1 (1.5±1.7) compared to Facility 2 (0.9±1.2, p<0.001). At Facility 1, predictors of in-degree centrality were younger age (β -0.03, 95% Confidence Interval (CI) [-0.05 to -0.014]; p=0.004), female sex (β 0.51, 95% CI [0.00 to 1.02]; p=0.04), and knowing more successful KT recipients (β 0.13, 95% CI [0.03 to 0.23]; p=0.01). Conversely at Facility 2, those who knew fewer successful KT recipients had higher in-degree centrality (β -0.92, 95% CI [-1.84 to 0.00]; p=0.048).  

Conclusion: Observed differences in social network structure could explain facility-level differences in KT attitudes. At Facility 1, the most influential patients (in-degree centrality) knew many successful KT recipients and may disseminate positive attitudes towards KT throughout the network. On the contrary, influential patients at Facility 2 knew fewer KT recipients and may disseminate negative attitudes towards KT. These findings help guide future network interventions targeting patients with high in-degree centrality and understanding how their attitudes towards KT influence the overall network.

A Social Network Analysis of Hemodialysis Clinics: Attitudes Towards Living Donor Kidney Transplant Among Influential Patients

Background:
In September 2021, a task force of kidney experts recommended a new eGFR formula without a race term to replace the previous race-based CKD-EPI formula. Existing studies have not assessed the effect of the new formula on the timeliness of chronic kidney disease (CKD) patients’ first nephrology clinic visit, a factor which has been shown to be associated with slowed progression and reduced costs of CKD. In this study we aimed to examine changes in the racial disparity between Black and non-Black patients in the timeliness of CKD patients’ first nephrology clinic visit before and after the implementation of the new race-free eGFR formula.
Methods:
We included adults seen at any of Indiana University Health’s 17 nephrology clinics between April 1st, 2021, and March 15th, 2023 for CKD or low GFR, excluding inpatient referrals and those who had previously seen an IU Health nephrologist. We conducted a multivariate linear regression with covariates including sex, self-reported race, age, timeframe, BMI, and an interaction term between race and timeframe, with timeframe defined as before or after implementation of the new formula on April 1st, 2022. Timeliness of care was defined as receiving care earlier in CKD progression (i.e., higher eGFR). All eGFR values shown are ml/min/1.73m2.
Results:
On average, throughout the entire study period, eGFR was 13.0 (95%CI: 10.3, 15.7) higher for female than male patients, 6.7 (95%CI: 2.4, 10.9) higher for non-Black than Black patients, and 5.9 (95%CI: 0.3, 11.4) higher for patients seen after the formula change than patients seen before the change. Each additional year of life reduced eGFR at time of first visit by -0.3 (95%CI: -0.4, -0.2). Our analysis found no evidence that the gap in eGFR at time of first visit between Black and non-Black patients changed significantly after the formula change (-3.7, 95%CI: -10.1, 2.7).
Conclusion:
The removal of the race term from the eGFR formula aimed to address systemic racial disparities in the diagnosis and treatment of CKD. Our results indicate that it instead led to earlier consultation with a nephrologist for all patients, as evidenced by a higher mean eGFR at the time of first clinic visit after the formula change. Despite this general trend, Black patients continued to have a lower mean eGFR at the time of first clinic visit than non-Black patients after the formula change, indicating a persistent delay in the involvement of outpatient nephrology for Black CKD patients.


Impact of race-free estimated Glomerular Filtration Rate (eGFR) on racial disparity in receiving timely outpatient nephrology care

Background:
Acute kidney injury is associated with significant morbidity and mortality. Estimation of volume status plays a major role in the differentiation of acute kidney injury. Invasive right heart catheterization has been traditionally used to guide management. We examined whether a simple bedside ultrasound assessment of intra-abdominal organ congestion can address this question and help differentiate acute kidney injury.
Methods:
A prospective single-center study included patients with cardiorenal, hepatorenal, acute tubular necrosis (ATN), and volume depletion acute kidney injury (AKI). Arrhythmias and body mass index (BMI)>30 were exclusions. Clinical data were obtained via chart review and urine microscopy was performed on all the patients. Inferior vena cava diameter (IVC); hepatic vein, portal vein and renal vein patterns; and Renal Resistive Indices (RRI) were obtained on three consecutive days via Logic e BT-12 ultrasound. Sonographers underwent training for eight hours and completed five ultrasound assessments prior to the study. Sonographers were also blinded from clinical diagnoses. We used analysis of variance (ANOVA) for continuous variables and chi-square tests for categorical variables.
Results:
A total of 81 AKI subjects were included, 34% with volume depletion, 19% cardiorenal, 35% ATN, 6% hepatorenal, and 3% other. The IVC diameters on the day of diagnosis in ATN, volume depletion, and cardiorenal were 2, 1.87, and 2.34 cm respectively (p=0.05). Diminished or reversed systolic hepatic duplex was seen in only 2 patients with volume depletion(p=0.002), and both these patients had moderate tricuspid regurgitation and elevated pulmonary arterial pressures on echocardiogram explaining the deviation. Similar hepatic duplex findings were noted on day 2 and day 3. The portal vein was continuous in 62.1% ATN, 60.7% volume depletion, and 31.3% cardiorenal patients (p=0.09). In comparison to patients in the cardiorenal group, portal pulsatility in the volume depletion arm was almost always less than 50% (p=0.49). Renal venous flow was continuous in 18.8% of the cardiorenal patients(p=0.18). Quality assessment was performed and estimated concordance with the expert review was 96.6, 94.4, 95.5, and 75.6 percent in IVC, hepatic vein, portal vein, and renal vein interpretations respectively.
Conclusion:
Based on our results, we propose that the Hepatic vein, IVC, and marked portal pulsatility(>50%) can help differentiate volume depletion from cardiorenal syndrome. This noninvasive bedside tool in addition to urine sediment can help differentiate acute kidney injury and future trials will examine management.

A pragmatic study utilizing bedside ultrasound to replace invasive interventions.

**Scoring is only possible in the semifinals hall**

**Abstract:**



Bilateral Pulvinar Responsive Stimulation for Broad Onset Drug Resistant Epilepsy

Objective:
Chordomas are rare primary tumors of the skull base, sacrum and vertebral column, derived from remnants of the embryonic notochord. Treatment options consist of surgical resection, and radiotherapy, with few chemotherapy options available. This study reviews clinical trials focused on radiotherapy modalities, such as photon therapy and particle therapy, as well as the concomitant use of radiation sensitizers.

Methods:
Literature review on all published clinical trials studying the usage of photon, proton, and carbon ion radiotherapy for chordoma in adults, as well as all published literature on radiation sensitizers used for treatment of chordoma from 2000 to 2023. Reviewed all of the current clinical trials on radiotherapy for chordoma in adults. 

Results:
We found 8 clinical trials, which include data from 1 observational study, 1 pilot study, 2 Phase I studies, 2 Phase I-II studies, and 7 Phase II studies. Of the completed clinical trials, 2 focused on protons alone, 2 focused on combined proton-photon radiotherapy, and 4 focused on carbon ion radiotherapy. The lowest radiation dosage was 48.0 GyE for carbon ion radiotherapy and highest was 79.2 GyE using protons only. All clinical trials reported an overall survival rate above 50% at 3-year follow-up. Nine publications were found on the use of radiation sensitizers for chordomas, 6 of which included in vitro studies and 5 included in vivo studies, using both patient and xenograft models. 

Conclusion:
The completed clinical trials focus on evaluating the effectiveness of proton, photon, and carbon ion radiotherapy for treatment of skull-base, spine, and sacral chordoma. Current trials continue to focus on effectiveness of treatments as well as comparing the different forms of radiotherapy and determining appropriate doses. Research on radiation sensitizers for chordomas shows various therapies, ranging from hyperthermia to pharmaceutical options, that require further study. Altogether, research on radiotherapy and radiation sensitizers have been used alongside radiation with positive and promising results for chordoma.

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The Race to Save Lives: A Comparative Analysis of Lead Time for Detecting Clinical Deterioration between Clinicians and Artificial Intelligence

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