Rothman Index Research Articles

Early Warning Scores With and Without Artificial Intelligence

Early warning decision support tools to identify clinical deterioration in the hospital are widely used, but there is little information on their comparative performance. To compare 3 proprietary artificial intelligence (AI) early warning scores and 3 publicly available simple aggregated weighted scores. This retrospective cohort study was performed at 7 hospitals in the Yale New Haven Health System. All consecutive adult medical-surgical ward hospital encounters between March 9, 2019, and November 9, 2023, were included. Simultaneous Epic Deterioration Index (EDI), Rothman Index (RI), eCARTv5 (eCART), Modified Early Warning Score (MEWS), National Early Warning Score (NEWS), and NEWS2 scores. Clinical deterioration, defined as a transfer from ward to intensive care unit or death within 24 hours of an observation. Of the 362 926 patient encounters (median patient age, 64 [IQR, 47-77] years; 200 642 [55.3%] female), 16 693 (4.6%) experienced a clinical deterioration event. eCART had the highest area under the receiver operating characteristic curve at 0.895 (95% CI, 0.891-0.900), followed by NEWS2 at 0.831 (95% CI, 0.826-0.836), NEWS at 0.829 (95% CI, 0.824-0.835), RI at 0.828 (95% CI, 0.823-0.834), EDI at 0.808 (95% CI, 0.802-0.812), and MEWS at 0.757 (95% CI, 0.750-0.764). After matching scores at the moderate-risk sensitivity level for a NEWS score of 5, overall positive predictive values (PPVs) ranged from a low of 6.3% (95% CI, 6.1%-6.4%) for an EDI score of 41 to a high of 17.3% (95% CI, 16.9%-17.8%) for an eCART score of 94. Matching scores at the high-risk specificity of a NEWS score of 7 yielded overall PPVs ranging from a low of 14.5% (95% CI, 14.0%-15.2%) for an EDI score of 54 to a high of 23.3% (95% CI, 22.7%-24.2%) for an eCART score of 97. The moderate-risk thresholds provided a median of at least 20 hours of lead time for all the scores. Median lead time at the high-risk threshold was 11 (IQR, 0-69) hours for eCART, 8 (IQR, 0-63) hours for NEWS, 6 (IQR, 0-62) hours for NEWS2, 5 (IQR, 0-56) hours for MEWS, 1 (IQR, 0-39) hour for EDI, and 0 (IQR, 0-42) hours for RI. In this cohort study of inpatient encounters, eCART outperformed the other AI and non-AI scores, identifying more deteriorating patients with fewer false alarms and sufficient time to intervene. NEWS, a non-AI, publicly available early warning score, significantly outperformed EDI. Given the wide variation in accuracy, additional transparency and oversight of early warning tools may be warranted.

Deep-learning generated B-line score mirrors clinical progression of disease for patients with heart failure

BackgroundUltrasound can detect fluid in the alveolar and interstitial spaces of the lung using the presence of artifacts known as B-lines. The aim of this study was to determine whether a deep learning algorithm generated B-line severity score correlated with pulmonary congestion and disease severity based on clinical assessment (as identified by composite congestion score and Rothman index) and to evaluate changes in the score with treatment. Patients suspected of congestive heart failure underwent daily ultrasonography. Eight lung zones (right and left anterior/lateral and superior/inferior) were scanned using a tablet ultrasound system with a phased-array probe. Mixed effects modeling explored the association between average B-line score and the composite congestion score, and average B-line score and Rothman index, respectively. Covariates tested included patient and exam level data (sex, age, presence of selected comorbidities, baseline sodium and hemoglobin, creatinine, vital signs, oxygen delivery amount and delivery method, diuretic dose).ResultsAnalysis included 110 unique subjects (3379 clips). B-line severity score was significantly associated with the composite congestion score, with a coefficient of 0.7 (95% CI 0.1–1.2 p = 0.02), but was not significantly associated with the Rothman index.ConclusionsUse of this technology may allow clinicians with limited ultrasound experience to determine an objective measure of B-line burden.

The Rothman Index predicts unplanned readmissions to intensive care associated with increased mortality and hospital length of stay: a propensity-matched cohort study

BackgroundPatients with unplanned readmissions to the intensive care unit (ICU) are at high risk of preventable adverse events. The Rothman Index represents an objective real-time grading system of a patient’s clinical condition and a predictive tool of clinical deterioration over time. This study was designed to test the hypothesis that the Rothman Index represents a sensitive predictor of unanticipated ICU readmissions.MethodsA retrospective propensity-matched cohort study was performed at a tertiary referral academic medical center in the United States from January 1, 2022, to December 31, 2022. Inclusion criteria were adult patients admitted to an ICU and readmitted within seven days of transfer to a lower level of care. The control group consisted of patients who were downgraded from ICU without a subsequent readmission. The primary outcome measure was in-hospital mortality or discharge to hospice for end-of-life care. Secondary outcome measures were overall hospital length of stay, ICU length of stay, and 30-day readmission rates. Propensity matching was used to control for differences between the study cohorts. Regression analyses were performed to determine independent risk factors of an unplanned readmission to ICU.ResultsA total of 5,261 ICU patients met the inclusion criteria, of which 212 patients (4%) had an unanticipated readmission to the ICU within 7 days. The study cohort and control group were stratified by propensity matching into equal group sizes of n = 181. Lower Rothman Index scores (reflecting higher physiologic acuity) at the time of downgrade from the ICU were significantly associated with an unplanned readmission to the ICU (p < 0.0001). Patients readmitted to ICU had a lower mean Rothman Index score (p < 0.0001) and significantly increased rates of mortality (19.3% vs. 2.2%, p < 0.0001) and discharge to hospice (14.4% vs. 6.1%, p = 0.0073) compared to the control group of patients without ICU readmission. The overall length of ICU stay (mean 8.0 vs. 2.2 days, p < 0.0001) and total length of hospital stay (mean 15.8 vs. 7.3 days, p < 0.0001) were significantly increased in patients readmitted to ICU, compared to the control group.ConclusionThe Rothman Index represents a sensitive predictor of unanticipated readmissions to ICU, associated with a significantly increased mortality and overall ICU and hospital length of stay. The Rothman Index should be considered as a real-time objective measure for prediction of a safe downgrade from ICU to a lower level of care.

Evaluation of the Rothman Index in Predicting Readmission after Colorectal Resection

The Rothman Index (RI) is a real-time health indicator score that has been used to quantify readmission risk in several fields but has never been studied in gastrointestinal surgery. In this retrospective single-institution study, the association between RI scores and readmissions after unplanned colectomy or proctectomy was evaluated in 427 inpatients. Patient demographics and perioperative measures, including last RI, lowest RI, and increasing/decreasing RI score, were collected. In the selected cohort, 12.4% of patients were readmitted within 30 days of their initial discharge. Last RI, lowest RI, decreasing RI, and increasing RI scores remained significant after controlling for covariates in separate multivariate regression analyses. The last RI score at the time of discharge was found to be the most strongly associated with 30-day readmission risk following colorectal resection. These findings support the RI as a potential tool in the inpatient management of postoperative patients to identify those at high risk of readmission.

Association of palliative care and hospital outcomes among solid tumour oncology inpatients

ObjectivesWe aimed to explore the association between receiving an inpatient palliative care consultation and hospital outcomes, including in-hospital death, intensive care unit (ICU) use, discharge to hospice, 30-day readmissions and...

Factors Associated With Inpatient Hospice Utilization Among Hospitalized Decedents With Comfort Measures Only Status.

Background: Patients with serious illness may elect to transition their care to comfort measures only (CMO) while in the hospital. Although studies have shown that routine hospice care is underutilized, the rate of general inpatient hospice (GIP) use among CMO patients during their terminal admission remains unclear. Objectives: We sought to (1) examine the rate of GIP utilization and (2) identify factors associated with its use among hospitalized CMO decedents. Methods: CMO decedents in two academic, tertiary care hospitals in the United States who died between October 1, 2020 and October 31, 2021, were subgrouped based on their primary medical service (GIP vs. non-GIP) at the time of inpatient death. Data abstracted from the electronic health record included demographics, primary diagnosis codes, Rothman Index (RI), time of CMO order, ordering clinician type, time of death, and length of stay (LOS). Multivariable logistic regression analysis was performed, adjusting for relevant covariates. Results: Of 1475 CMO decedents, only 321 (n = 22%) patients received GIP. On multivariable analysis, CMO patients who died in an ICU were five times less likely (odds ratio [OR] = 0.18, confidence interval [95% CI] 0.11-0.29) to receive GIP. Every 10-point increase in RI raised the likelihood of receiving GIP by 59% (OR = 1.59, 95% CI 1.39-1.80). Conclusions: Most CMO decedents died in the hospital without GIP. Compared with GIP decedents, non-GIP decedents were less acutely ill. There was no difference in total LOS between the two groups. CMO decedents were much less likely to receive GIP in an ICU. The RI may help clinicians identify CMO patients who would benefit from GIP earlier in their terminal admission.

An AI-driven clinical care pathway to reduce 30-day readmission for chronic obstructive pulmonary disease (COPD) patients

Healthcare regulatory agencies have mandated a reduction in 30-day hospital readmission rates and have targeted COPD as a major contributor to 30-day readmissions. We aimed to develop and validate a simple tool deploying an artificial neural network (ANN) for early identification of COPD patients with high readmission risk. Using COPD patient data from eight hospitals within a large urban hospital system, four variables were identified, weighted and validated. These included the number of in-patient admissions in the previous 6 months, the number of medications administered on the first day, insurance status, and the Rothman Index on hospital day one. An ANN model was trained to provide a predictive algorithm and validated on an additional dataset from a separate time period. The model was implemented in a smartphone app (Re-Admit) incorporating four input risk factors, and a clinical care plan focused on high-risk readmission candidates was then implemented. Subsequent readmission data was analyzed to assess impact. The areas under the curve of receiver operating characteristics predicting readmission with ANN is 0.77, with sensitivity 0.75 and specificity 0.67 on the separate validation data. Readmission rates in the COPD high-risk subgroup after app and clinical intervention implementation saw a significant 48% decline. Our studies show the efficacy of ANN model on predicting readmission risks for COPD patients. The AI enabled Re-Admit smartphone app predicts readmission risk on day one of the patient’s admission, allowing for early implementation of medical, hospital, and community resources to optimize and improve clinical care pathways.

Evaluation of Clinical Predictors for Major Outcomes in Patients Hospitalized With COVID-19: The Potential Role of the Rothman Index.

IntroductionThe Rothman Index (RI, PeraHealth, Inc. Charlotte, NC, USA) is a predictive model intended to provide continuous monitoring of a patient's clinical status. There is limited data to support its use in the risk stratification of patients infected with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). We hypothesized that low admission RI scores would correlate with higher rates of adverse outcomes in patients hospitalized for coronavirus disease 2019 (COVID-19).MethodsMedical records of adult patients admitted to a single 1,200-bed tertiary academic center were retrospectively reviewed for demographic data, baseline characteristics, RI scores, admission to intensive care unit (ICU), need for mechanical ventilation, and inpatient mortality. Statistical analyses were performed using STATA statistical software, version 17 (Stata Corp LLC, College Station, TX, USA). Continuous variables were analyzed using the Mann-Whitney test, and categorical variables were analyzed using Fisher’s exact test. Both univariate and multivariate analyses were performed. A p-value <0.05 was considered statistically significant.ResultsMedian admission RI score for the entire cohort was 63.0 (IQR 45.0 - 77.1). The cohort was divided by admission RI into a low-risk group (RI ≥70; n=70) and a high-risk group (RI <70; n=107). Compared to patients with low-risk RI, patients with high-risk RI had higher mortality (95.2%, 95% CI: 85.8 - 105 vs 4.8%, 95% CI: -5 - 14.2, p < 0.01), were more likely to require ICU admission (90.2%, 95% CI: 81.9 - 98.5 vs 9.8%, 95% CI: 1.5 - 18.1, p < 0.01) and mechanical ventilation (89.7%, 95% CI: 78.3 - 101 vs 10.3%, 95% CI: -1 - 21.7, p < 0.01), and had a longer median hospital length of stay (12 days, 95% CI: 9 - 14 vs 5 days, 95% CI: 4 - 7, p < 0.01).ConclusionsHigh-risk RI was associated with increased admission to the ICU, mechanical ventilation, and mortality. These results suggest that it may be used as a tool to aid provider judgment in the setting of COVID-19.

Artificial intelligence and clinical deterioration.

To provide an overview of the systems being used to identify and predict clinical deterioration in hospitalised patients, with focus on the current and future role of artificial intelligence (AI). There are five leading AI driven systems in this field: the Advanced Alert Monitor (AAM), the electronic Cardiac Arrest Risk Triage (eCART) score, Hospital wide Alert Via Electronic Noticeboard, the Mayo Clinic Early Warning Score, and the Rothman Index (RI). Each uses Electronic Patient Record (EPR) data and machine learning to predict adverse events. Less mature but relevant evolutions are occurring in the fields of Natural Language Processing, Time and Motion Studies, AI Sepsis and COVID-19 algorithms. Research-based AI-driven systems to predict clinical deterioration are increasingly being developed, but few are being implemented into clinical workflows. Escobar et al. (AAM) provide the current gold standard for robust model development and implementation methodology. Multiple technologies show promise, however, the pathway to meaningfully affect patient outcomes remains challenging.

Do In-Hospital Rothman Index Scores Predict Postdischarge Adverse Events and Discharge Location After Total Knee Arthroplasty?

There have been efforts to reduce adverse events and unplanned readmissions after total joint arthroplasty. The Rothman Index (RI) is a real-time, composite measure of medical acuity for hospitalized patients. We aimed to examine the association among in-hospital RI scores and complications, readmissions, and discharge location after total knee arthroplasty (TKA). We hypothesized that RI scores could be used to predict the outcomes of interest. This is a retrospective study of an institutional database of elective, primary TKA from July 2018 until December 2019. Complications and readmissions were defined per Centers for Medicare and Medicaid Services. Analysis included multivariate regression, computation of the area under the curve (AUC), and the Youden Index to set RI thresholds. The study cohort's (n= 957) complications (2.4%), readmissions (3.6%), and nonhome discharge (13.7%) were reported. All RI metrics (minimum, maximum, last, mean, range, 25th%, and 75th%) were significantly associated with increased odds of readmission and home discharge (all P < .05). RI scores were not significantly associated with complications. The optimal RI thresholds for increased risk of readmission were last ≤ 71 (AUC= 0.65), mean ≤ 67 (AUC= 0.66), or maximum ≤ 80 (AUC= 0.63). The optimal RI thresholds for increased risk of home discharge were minimum ≥ 53 (AUC= 0.65), mean ≥ 69 (AUC= 0.65), or maximum ≥ 81 (AUC= 0.60). RI values may be used to predict readmission or home discharge after TKA.

Evaluation of Clinical Predictors for Major Outcomes in Patients Hospitalized for COVID-19 Disease: The Potential Role for the Rothman Index

Evaluation of Clinical Predictors for Major Outcomes in Patients Hospitalized for COVID-19 Disease: The Potential Role for the Rothman Index

The Rothman Index Does Not Predict a Successful Extubation in the Neurosurgical Critical Care Unit.

BackgroundIdentification of risk factors associated with successful extubation in neurosurgical critical care units (NSICUs) has been elusive due to the complex nature of neurocritical care injuries and patient factors. Traditional risk factors for extubation were shown to have poor predictive value in neurocritical care patients as compared to mixed ICU patients. The aim of this study was to determine if any risk factors, including the Rothman Index, could reliably predict successful extubation in a large sample size of neurocritical care patients.MethodsWe retrospectively analyzed 610 consecutively intubated patients in an NSICU while collecting variables of interest in airway management. Furthermore, Rothman Indices were collected immediately after intubations and extubations. A paired t-test of the immediate changes in Rothman Indices after airway manipulation was compared in patients who needed reintubation. In a smaller cohort of 88 patients, in whom complete data points existed for airway management, we performed a principal component analysis (PCA) to determine which risk factors were associated with extubation success when indexed with the magnitude of the Rothman Index.ResultsIn 610 consecutively intubated patients, the mean pre-intubation Rothman Index average was 41.0 compared to the mean post-extubation Rothman Index was 35.4 (p<0.0001). Compared to those who were re-intubated, the Rothman Index did not correlate well with the prediction of extubation (p=0.355). Furthermore, an analysis of the PCA plot showed that a higher respiratory rate, longer length of stay, shorter length of intubation, and smaller cuff leak percentage were identified as risk factors associated with reintubation. Age and change in rapid shallow breathing index (RSBI) did not correlate with reintubation.ConclusionThe Rothman Index does not predict extubation success in patients in an NSICU. Risk factors associated with reintubation were respiratory rate, length of stay, length of intubation, and cuff leak percentage. Reintubation rates in our single-center NSICU are on par with general critical care populations.

Rothman Index as a Predictor of 30-Day Hospital Readmission

Introduction: The Centers for Medicare and Medicaid services financially penalize hospitals for elevated 30-day readmission rates. Identifying patients at high risk for short-term readmission would allow health systems to strategically allocate resources to this vulnerable population. The objective of this study was to determine whether there was a difference in mean Rothman Index value for patients readmitted to the hospital within 30 days of index stay versus patients not readmitted in order to evaluate the Rothman Index’s utility as a predictive tool. Materials and Methods: Data from 100 subjects from a single academic medical center, with a balanced number of readmit (n=50, mean age 68.9 years, 54% female) and non-readmits (n=50, 46% female, mean age 70.9 years), was collected. Results: Non-readmits demonstrated significantly higher mean Rothman Index values (70.94 ± 1.3) compared to patients readmitted within 30 days (mean Rothman Index of 61.68 ± 1.6) at (P&lt; .001; 95% CI, 5.10 to 13.41). Age (95% CI, -0.052 to 0.006; P= .12), gender (95% CI, -0.949 to 0.948; P= .99) and primary discharge diagnosis from index stay (P= 0.31) were not predictive of readmission; only the Rothman Index was (95% CI, -0.136 to -0.039; P&lt;.001).The coefficient of the Rothman Index was -0.088, indicating that for each 1 point increase in Rothman Index, a patient’s odds of readmission within 30 days declined by 8.8% (95% CI, -0.136 to -0.039; P&lt; .001). Conclusions: The Rothman Index can be utilized as a predictive tool to identify patients at high risk of unplanned 30-day hospital readmission, thereby allowing health systems to strategically allocate outside hospital resources.

Rothman Index As a Real-Time Predictor of Intensive Care Unit Admissions and Mortality in Acute Myeloid Leukemia Patients Undergoing Induction Chemotherapy

Introduction: In-hospital mortality is higher for patients (Pts) with Acute Myeloid Leukemia(AML) requiring intensive care unit(ICU) admissions (43.1% Vs 9.3%) as described by Halpren,A.B., et al (JAMA Oncol. 2017. 3(3): p. 374-381). Various demographic factors identify patients at risk but may not fully provide primary prevention strategies. The Rothman Index (RI) is a dynamic acuity metric that incorporates 26 variables of physiological measures, clinical assessments, and laboratory results into a composite index that is trended over time. In our study, we hypothesize that RI at admission for induction chemotherapy (IRx) and the number of unstable days defined as a drop in RI by 15 points in a 24-hour period; can predict ICU admissions. This is a score that is trended in real time providing potential opportunities for Pt stabilization and preventing ICU admissions.Methods: This is a single center retrospective observational study of AML pts admitted between 8/24/12 to 4/22/17. Data collection included age at diagnosis, RI at admission, unstable days, days of stability before first unstable day, total length of hospital stay (LOS) for IRx, days to first readmission, total number of unstable days for readmission and mortality. The primary end point was ICU admission. Continuous variables were reported as means and standard deviations or means with confidence intervals or median with inter quartile range(IQR). Dichotomous variables were reported as proportions. A logistic regression model was used to predict probability of ICU admission.Results: A total of 83 pts (54.21% males) were studied with a median age at diagnosis of 66 (IQR 19) years(y) and mean LOS of 21.79±16.19 days. Twenty (24.09%) pts had ICU stay in their index admission and the age at admission was very similar to the non-ICU group [Median 66y (IQR 21) Vs 67y (IQR 19)]. Nineteen pts died with a mortality rate of 22.89%. The RI at admission for the ICU group [Mean 73.34 (CI 63.14-83.54) Vs 82.74 (CI 79.72-85.76)] and days of stability before the first unstable day [Mean 2.89 (CI 1.05-4.73) Vs 5.63 (CI 4.23-7.03)] were lower. Number of unstable days [Mean 8.57 (CI 6.20-10.95) Vs 3.36 (CI 2.32-4.40)] and LOS for the IRx admission [Mean 26.10 (CI 20.23-31.97) Vs 20.38 (CI 16.05-24.70)] was higher in the ICU group. Number of unstable days was the best predictor of ICU admissions [odds ratio 1.25 (CI 1.10-1.41 p 0.00)] as well as days of stability before first unstable day [odds ratio 0.83 (CI 0.70-0.98 p 0.03)] and RI at admission [odds ratio 0.96 (CI 0.93-0.99 p 0.04)]. However, as mortality predictors, number of unstable days [odds ratio 1.08 (CI 0.96-1.21 p 0.18)], days of stability before first unstable day [odds ratio 1.06 (CI 0.95- 1.18 p 0.28)] and RI at admission [odds ratio (CI 0.96-1.04 p 0.74)] did not meet statistical significance and this could be due to the smaller sample size.Conclusions:Real time trends in RI can potentially predict AML pts undergoing IRx that may need critical care. Higher number of unstable RI days along with fewer stable days before first unstable day predict higher likelihood for ICU stay. Finally, RI also can predict the longer LOS in patients with ICU admissions. The Clinical implications of using RI can be far reaching as it provides a real-time opportunity for utilizing clinical resources to stabilize pts on the floor, prevent ICU admissions and reduce LOS. DisclosuresIyer:Genentech: Research Funding; Takeda: Research Funding.

COVID-19 highlights the need to optimize critical care resource use: The role of a respiratory-led multidisciplinary team.

COVID-19 highlights the need to optimize critical care resource use: The role of a respiratory-led multidisciplinary team.

Palliative Referrals in Advanced Cancer Patients: Utilizing the Supportive and Palliative Care Indicators Tool and Rothman Index

Timely identification of palliative care needs can reduce hospitalizations and improve quality of life. The Supportive & Palliative Care Indicators Tool (SPICT) identifies patients with advanced medical conditions who may need special care planning. The Rothman Index (RI) detects patients at high risk of acutely decompensating in the inpatient setting. SPICT and RI among cancer patients were utilized in this study to evaluate their potential roles in palliative care referrals. Advanced cancer patients admitted to an institution in Baltimore, Maryland in 2019 were retrospectively reviewed. Patient demographics, length of hospital stay (LOS), palliative care referrals, RI scores, and SPICT scores were obtained. Patients were divided into SPICT positive or negative and RI > 60 or RI < 60.Unpaired t-tests and chi-square tests were utilized to determine the associations between SPICT and RI and early palliative care needs and mortality. 227 patients were included, with a mean age of 68 years, 63% Black, 59% female, with the majority having lung and GI malignancies. Sixty percent were SPICT +, 21% had RI < 60. SPICT + patients were more likely to have RI < 60 (p = 0.001). SPICT + and RI < 60 patients were more likely to have longer LOS, change in code status, more palliative/hospice referrals, and increased mortality (p <0.05). SPICT and RI are valuable tools in predicting mortality and palliative/hospice care referrals. These can also be utilized to initiate early palliative and goals of care discussions in patients with advanced cancer.

Stratifying Deterioration Risk by Acuity at Admission Offers Triage Insights for Coronavirus Disease 2019 Patients.

Triaging patients at admission to determine subsequent deterioration risk can be difficult. This is especially true of coronavirus disease 2019 patients, some of whom experience significant physiologic deterioration due to dysregulated immune response following admission. A well-established acuity measure, the Rothman Index, is evaluated for stratification of patients at admission into high or low risk of subsequent deterioration. Multicenter retrospective study. One academic medical center in Connecticut, and three community hospitals in Connecticut and Maryland. Three thousand four hundred ninety-nine coronavirus disease 2019 and 14,658 noncoronavirus disease 2019 adult patients admitted to a medical service between January 1, 2020, and September 15, 2020. None. Performance of the Rothman Index at admission to predict in-hospital mortality or ICU utilization for both general medical and coronavirus disease 2019 populations was evaluated using the area under the curve. Precision and recall for mortality prediction were calculated, high- and low-risk thresholds were determined, and patients meeting threshold criteria were characterized. The Rothman Index at admission has good to excellent discriminatory performance for in-hospital mortality in the coronavirus disease 2019 (area under the curve, 0.81-0.84) and noncoronavirus disease 2019 (area under the curve, 0.90-0.92) populations. We show that for a given admission acuity, the risk of deterioration for coronavirus disease 2019 patients is significantly higher than for noncoronavirus disease 2019 patients. At admission, Rothman Index-based thresholds segregate the majority of patients into either high- or low-risk groups; high-risk groups have mortality rates of 34-45% (coronavirus disease 2019) and 17-25% (noncoronavirus disease 2019), whereas low-risk groups have mortality rates of 2-5% (coronavirus disease 2019) and 0.2-0.4% (noncoronavirus disease 2019). Similarly large differences in ICU utilization are also found. Acuity level at admission may support rapid and effective risk triage. Notably, in-hospital mortality risk associated with a given acuity at admission is significantly higher for coronavirus disease 2019 patients than for noncoronavirus disease 2019 patients. This insight may help physicians more effectively triage coronavirus disease 2019 patients, guiding level of care decisions and resource allocation.

10: Early Identification of COVID-19 Patients Susceptible to Rapid Deterioration Using the Rothman Index

Beals, Joseph1; Belk, Kathy1; Rothman, Michael1; Rimar, Joan2; Donohue, Thomas2; Hoq, Mahfuz3 Author Information

Admission Rothman Index, Aspirin, and Intermediate Dose Anticoagulation Effects on Outcomes in COVID-19: A Multi-Site Propensity Matched Analysis

Introduction: Venous thromboembolism and in-situ small vessel thrombosis are increased in hospitalized patients with COVID-19 in several patient cohorts. Endotheliopathy and activation of both platelets and coagulation predict critical illness and death. For these reasons the use of anti-platelet agents and increased-intensity anticoagulation in the care of hospitalized patients with COVID-19 is under intense study in several clinical trials. We sought to examine the impact of aspirin and anticoagulation on hospitalization outcomes.Methods: We examined outcomes in a large multi-site cohort of consecutive, hospitalized, COVID-19 laboratory confirmed patients under a risk-stratified treatment algorithm from March 13 through June 18, with a focus on efficacy of aspirin and/or increased-intensity anticoagulation. Out of 4150 identified hospitalized patients with COVID-19, we created 3 study cohorts. The overall cohort (2785 patients) excluded pediatric patients, those with incomplete electronic data, and those with multiple admissions. The aspirin (1956 patients) and anticoagulation (1623 patients) cohorts were nested within the overall cohort; the former excluded patients on any home anti-platelet therapy or those who received non-aspirin anti-platelet therapy in the hospital, while the latter excluded patients who did not receive prophylactic or intermediate dose anticoagulation in the hospital. The primary outcome was in-hospital death. Secondary outcomes were time-to-death with a competing risk (time-to-hospital-discharge), escalation to ICU, length-of-stay and use of mechanical ventilation. Variables examined included age, gender, BMI, race, Rothman Index (RI), D-dimer (DD) and patient co-morbidities including cardiovascular disease, chronic kidney disease, and prior VTE. The aspirin and anticoagulation cohorts underwent propensity score (PS) matching utilizing variables found to be significant in multivariable regression modeling in the overall cohort with 638 and 386 patients, respectively.Results: Univariate followed by multivariable regression modeling in the 2785 patient overall cohort established a novel role for RI, and independent roles for age, BMI, and maximum DD, in predicting severity of illness. In all cohorts the 50th and lower percentile of admission RI was predictive of mortality in multivariable modeling (i.e. aspirin: 3rd and 4th admission RI quartiles with HR = 0.18 for both, p<0.001 for both). In PS matched patients, aspirin was associated with a significant decrease in mortality (OR 0.65 [0.42, 0.98], p=0.044) and a significant increase in mechanical ventilation (OR 1.49 [1.03, 2.18], p=0.037) and ICU status (OR = 1.45 [1.06, 1.98], p=0.021). In PS matched patients in the anticoagulation cohort, intermediate versus prophylactic dose anticoagulation was associated with a marginal decrease in mortality (OR 0.60, p=0.053). In the aspirin cohort examining in-hospital death and discharge as competing risks, the use of aspirin was associated with decreased mortality (p=0.042) and had no effect on discharge (p=0.31). In the anticoagulation cohort a similar competing risk model showed the use of intermediate rather than prophylactic anticoagulation decreased mortality (p=0.046) and had no effect on discharge (p = 0.21).Conclusion: We show in a large cohort of consecutively hospitalized patients with COVID-19 treated under a risk-stratified algorithm the prognostic utility of the admission RI in assessing outcomes in hospitalized patients with COVID-19 and a potential benefit of aspirin therapy on in-hospital death from COVID-19. A potential albeit marginal benefit of intermediate dose anticoagulation over prophylactic dose anticoagulation merits further study with results of clinical trials awaited.FigureDisclosuresNeuberg:Pharmacyclics: Research Funding; Madrigak Pharmaceuticals: Current equity holder in publicly-traded company; Celgene: Research Funding.

Palliative referrals in advanced cancer patients: Utilizing the Supportive and Palliative Care Indicators Tool and Rothman Index.

12102 Backgroun1d: Timely identification of palliative care needs have the ability to reduce hospitalizations and improve QOL. The Supportive &amp; Palliative Care Indicators Tool (SPICT) is used to identify patients with advanced stage medical conditions who may need special care planning. The Rothman Index (RI) detects patients at high risk of acutely decompensating in the inpatient setting and has been validated to assess 24-hour mortality risk. We used SPICT and RI in cancer patients admitted to the hospital and evaluated their roles in recognizing early palliative care needs and 6-month mortality. Methods: Advanced/metastatic cancer patients admitted to our institution from Jan 1, 2019 to June 30, 2019 were retrospectively reviewed. Patient demographics, length of hospital stay (LOS), comorbidities, palliative/hospice care referrals, vital status, initial RI score, and computed SPICT scores were obtained. Worse clinical indicators were defined as SPICT positive if it met &gt; 2 clinical indicators or RI &lt; 60. Univariate and bivariate analyses were performed. Results: A total of 227 patients were included, mean age 68, 34% Caucasians, 63% Blacks, 59% female, median comorbidities of 3, with majority having lung and GI malignancies. A total of 137 (60%) were SPICT +, 47 (21%) had RI &lt; 60, and 38 (17%) concurrent SPICT + and RI &lt; 60. SPICT + patients were more likely to have longer hospital stay, change in code status, more palliative/hospice referrals, and increased mortality. Those with RI &lt; 60 had similar results (Table). SPICT + patients are more likely to have RI &lt; 60 (p = 0.0013). Conclusions: SPICT and RI are valuable tools in predicting 6-month mortality and palliative/hospice care referrals. These can also be utilized to initiate early palliative and goals of care discussions in patients with advanced cancer. [Table: see text]