Rothman Index Research Articles

Staff Developed PACU Acuity Scoring Grid.

The goal of this project was to design a tool to classify patients in the postanesthesia care unit (PACU) for acuity as defined by nursing interventions. An instrument development and validation approach was used. A PACU acuity scoring grid was developed using the American Society of PeriAnesthesia Nurses (ASPAN) professional guidelines and Rothman Index concepts to help classify patients by acuity and determine PACU acuity class. The appropriate ASPAN nurse-patient ratio was assigned to the classification. PACU staff were educated on use of the PACU acuity scoring grid. Staff piloted the grid on patients in PACU and then refined the grid. Validity and reliability of the grid were also evaluated. Data evaluation showed that 54% of patients fell into classification II, with a ratio of one nurse to two patients. Classification III is the second highest category with 38%, with a ratio of one nurse to one patient. The tool demonstrated validity and the Cronbach alpha measure of reliability for the PACU acuity scoring grid was 0.695 on 73 of the 92 variables on the grid. The PACU acuity scoring grid is a tool that can be used to identify patient acuity by assigning acuity points to nursing interventions in five categories. The acuity points can be used to assign a patient to a PACU class, which can then converted to a nurse-patient ratio to allow for the provision of safe and efficient nursing care.

Predictive Value of Rothman Index in Patients That Develop Febrile Neutropenia

Background: Febrile Neutropenia (FN) in cancer patients is associated with a high mortality rate yet identifying patients with FN at highest risk for short term morbidity and mortality remains challenging. The Rothman Index (RI) is a real-time composite measurement of a patient's condition using 26 clinical variables sensitive to signs of patient clinical decline. RI has been shown to predict intensive care unit (ICU) admission, hospital readmission and short term mortality among inpatients. A prior study from our institution demonstrated that hospitalized patients with hematological malignancies and RI < 60 have a greater likelihood of readmission or discharge to hospice rather than those with higher RI scores. The aim of this study was to explore RI as a predictor of adverse outcomes in patients with hematological malignancies who have FN.Methods: Chart records of all patients with hematological malignancies and FN who were hospitalized at Yale Cancer Center between February of 2013 and July of 2014 were reviewed. Clinical variables collected included demographics, malignancy type, comorbidities, admission condition and microbiological data related to the FN episode. The primary outcome measure was a composite of in-hospital mortality and discharge to hospice care. Variables associated with the primary outcome in univariate analysis were entered into a multivariate logistic regression model for analysis.Results: Of 308 patients with hematological malignancies, 180 (58.4%) developed FN during their admission and 38 (12.3%) died during the admission or were discharged to hospice. A total of 85 (27.6%) patients had a RI < 60, 155 (50.3%) had a RI between 60-80, and 71 (23.1%) had a RI > 80. Controlling for malignancy type, major medical comorbidities and microbiologically confirmed bacterial infection, RI < 60 was independently associated with this outcome (Odds ratio 3.02, 95% Confidence Interval 1.25-7.34). Baseline renal disease, pneumonia, respiratory failure and sepsis were also associated with the primary outcome.Conclusion: RI is an objective measure that can independently predict poor outcomes among inpatients with FN. RI can guide clinicians caring for patients with FN in both prognostication and identifying high risk patients with an RI < 60 who are at higher risk for mortality, discharge to hospice, or high risk of readmission. The utility of this scoring system should be compared to existing risk stratification systems used in Oncology to identify its optimal use in the clinical setting.Table 1Factors associated with inpatient mortality or hospice discharge among hematologic malignancy inpatients with febrile neutropeniaVariableOR95% CI for ORpRI < 603.021.25-7.34.015Respiratory Failure3.511.07-11.56.039Renal Disease4.971.38-17.9.014Sepsis8.181.63-41.2.011Pneumonia8.011.42-45.1.018 DisclosuresNo relevant conflicts of interest to declare.

Evaluation of Electronic Medical Record Vital Sign Data Versus a Commercially Available Acuity Score in Predicting Need for Critical Intervention at a Tertiary Children’s Hospital

Evaluate the ability of vital sign data versus a commercially available acuity score adapted for children (pediatric Rothman Index) to predict need for critical intervention in hospitalized pediatric patients to form the foundation for an automated early warning system. Retrospective review of electronic medical record data. Academic children's hospital. A total of 220 hospitalized children 6.7 ± 6.7 years old experiencing a cardiopulmonary arrest (condition A) and/or requiring urgent intervention with transfer (condition C) to the ICU between January 2006 and July 2011. None. Physiologic data 24 hours preceding the event were extracted from the electronic medical record. Vital sign predictors were constructed using combinations of age-adjusted abnormalities in heart rate, systolic and diastolic blood pressures, respiratory rate, and peripheral oxygen saturation to predict impending deterioration. Sensitivity and specificity were determined for vital sign-based predictors by using 1:1 age-matched and sex-matched non-ICU control patients. Sensitivity and specificity for a model consisting of any two vital sign measurements simultaneously outside of age-adjusted normal ranges for condition A, condition C, and condition A or C were 64% and 54%, 57% and 53%, and 59% and 54%, respectively. The pediatric Rothman Index (added to the electronic medical record in April 2009) was evaluated in a subset of these patients (n = 131) and 16,138 hospitalized unmatched non-ICU control patients for the ability to predict condition A or C, and receiver operating characteristic curves were generated. Sensitivity and specificity for a pediatric Rothman Index cutoff of 40 for condition A, condition C, and condition A or C were 56% and 99%, 13% and 99%, and 28% and 99%, respectively. A model consisting of simultaneous vital sign abnormalities and the pediatric Rothman Index predict condition A or C in the 24-hour period prior to the event. Vital sign only prediction models have higher sensitivity than the pediatric Rothman Index but are associated with a high false-positive rate. The high specificity of the pediatric Rothman Index merits prospective evaluation as an electronic adjunct to human-triggered early warning systems.

Impact of Severity-Adjusted Workload on Health Status of Patients Discharge from an ICU

We examine whether workload has an impact on a direct measure of the health status of patients discharged from Intensive Care Units (ICUs). We use data collected from the medical ICU and the surgical ICU of a major teaching hospital and a relatively new measure of patient acuity called the Rothman Index (RI). The RI is frequently updated during a patient’s hospital stay, which enables us to track patients health status very close to the time of their ICU discharge. Leveraging the RI, we measure ICU workload in a novel way that takes into account not only the census but also patient acuity. To our knowledge, this is the first study to show that more acutely ill patients are discharged from an ICU when the severity-adjusted workload is high rather than low. Further, we find that higher severity-adjusted workload is associated with ICU discharge times that start earlier and end later, a shorter ICU length-of-stay (LOS), and an increased likelihood of discharge to a step-down unit. We also find that downstream unit census influences the effect of workload on health status at ICU discharge.

Using the Rothman Index to Predict Discharge and Readmission Rates in an Inpatient Hematology Unit

BACKGROUND: Limited data exists regarding predictors of discharge (DC) disposition and readmission (RA) rates for hematology-oncology patients who are hospitalized. The Rothman Index (RI) is a single numerical score ranging from -91 to 100 derived from 26 data elements including vital signs, laboratory results, and nursing assessments. It is automatically calculated and displayed in real-time in the electronic medical record by a commercial software system. A previous analysis at our institution revealed a significant association between RI and DC disposition, and between RI and RA rates, for patients with solid tumors who were hospitalized in a dedicated inpatient oncology unit. We sought to determine the effectiveness of RI in predicting DC disposition and RA rates for hospitalized patients with hematologic malignancies.METHODS: Medical records of patients with hematologic malignancies admitted to Yale Cancer Center between 3/1/13 and 2/28/14 were reviewed. Admissions for elective chemotherapy were excluded. DC disposition was categorized as home (Hm), nursing home (NH), hospice (Hp), or death (D). RI at DC was examined as a predictor of DC disposition, overall RA rate, and RA rate of patients discharged to Hm. As an individual patient might have several admissions, generalized estimating equation models were used to assess the effect of RI at DC on DC disposition and the possibility of RA by controlling the within subject correlation.RESULTS: A total of 281 patients comprising 520 admissions were included in the final analysis. The median age was 59 (range: 19-93; age 70 or older: 21%), and 53% of patients were male. The most common hematologic malignancies were non-Hodgkin lymphoma (33%), acute myeloid leukemia (25%), and multiple myeloma (19%). Among all admissions, DC disposition was as follows: Hm, 439 (84%); NH, 53 (10%); Hp, 18 (4%); and D, 10 (2%). RI at DC was a significant predictor of DC disposition (p = 0.0001), with median RI at DC as follows: Hm, 82.1; NH, 61.4; Hp, 38.7; and D, -1.3. Patients with lower RI at DC were significantly more likely to be readmitted (p = 0.0006), even when the analysis was restricted to patients who had been discharged to Hm (p = 0.0131). Among Hm patients, RA rate was 61% for those whose RI at DC was < 60 and 46% for RI at DC > 60.CONCLUSIONS: The RI at DC significantly correlated with patient DC disposition and RA rates and may assist health care providers in DC planning with the goal of reducing hospital RA rates and ultimately health care costs. DisclosuresLee:Pfizer: Consultancy.

Characterization of aggressive interventions within 30 days of death in lung cancer patients at Smilow Cancer Hospital (SCH).

20 Background: Multiple studies have demonstrated that patients with advanced cancer receive overly aggressive care at the end-of-life. With the goal of ultimately reducing overutilization, we sought to characterize the care we provide and identify if the Rothman Index (RI), a validated EMR tool which displays patients’ clinical condition, could predict those in whom aggressive interventions might be futile. Methods: We performed a retrospective chart review on 118 patients with advanced lung cancer who died at SCH in the fiscal year 2012. We extracted: 1. Rates of aggressive interventions including ICU stay, chemotherapy, non-palliative radiation and surgery within 30 days of death; 2. Rate of palliative care (PC) consultation; 3. RI. 4. Readmission rates. Results: 118 lung cancer patients were identified: 88% stage IV, 12% stage III or unstaged. Results are characterized in the Table. Conclusions: Consistent with national trends, utilization of aggressive interventions near the end-of-life is high amongst SCH advanced lung cancer patients. Discordant with ASCO and IOM guidelines, which call for PC in all patients with advanced cancer, PC was involved in only 29% of our study population, which is an opportunity for quality improvement. A RI of 40 appears to be associated with death within 30 days. Further validation of RI may serve as a trigger to direct care toward palliative measures. Going forward we will expand this analysis to our entire cancer population to provide a baseline for future quality interventions. [Table: see text]

Using the Rothman index to predict early unplanned surgical intensive care unit readmissions.

The Rothman index (RI) is a numerical score calculated hourly from 26 data points in the electronic medical record by a commercial software package. Although it is purported to serve as an indicator of change in a patient's condition, it has not been extensively evaluated in the literature. Our objective was to determine whether the RI can be used to predict early surgical intensive care unit (SICU) readmissions. This is a single-institution, retrospective 12-month period review of all patients transferred from the SICU to the surgical floor. Patients readmitted to the SICU within 48 hours were compared with patients who did not require readmission during this time (control). Demographics and continuous RI scores were collected at admission, 24 hours before SICU transfer, and for the first 48 hours on the surgical floor or until readmission to the SICU. A total of 1,152 SICU patients were transferred to the surgical floor; 27 patients were readmitted within 48 hours of transfer. Demographics were similar in both groups. The SICU length of stay was longer in the readmission group (mean [SD], 4.7 [8.1] vs. 16.5 [15.2]; p < 0.001). The RI immediately before SICU transfer was higher in the control group (70.4 [20.3] vs. 49.1 [20.9], p < 0.001) and was uniformly improved from the RI at the initial SICU admission. In comparison, readmitted patients had more variable RI trends from admission to SICU transfer (mean Δ, 6.51; range, -54.10 to 48.6), and 40.74% of readmitted patients actually had a decreased RI score on transfer. No patient with a RI score greater than 82.90 required readmission within 48 hours. An increased RI score or a score greater than 82.90 correlates with appropriateness for SICU transfer to the surgical floor. A decreased RI score is strongly associated with SICU readmission within 48 hours and should be explored as a potential quality metric. Epidemiologic/prognostic study, level III.

Measuring the modified early warning score and the Rothman Index: Advantages of utilizing the electronic medical record in an early warning system

Early detection of an impending cardiac or pulmonary arrest is an important focus for hospitals trying to improve quality of care. Unfortunately, all current early warning systems suffer from high false-alarm rates. Most systems are based on the Modified Early Warning Score (MEWS); 4 of its 5 inputs are vital signs. The purpose of this study was to compare the accuracy of MEWS against the Rothman Index (RI), a patient acuity score based upon summation of excess risk functions that utilize additional data from the electronic medical record (EMR). MEWS and RI scores were computed retrospectively for 32,472 patient visits. Nursing assessments, a category of EMR inputs only used by the RI, showed sharp differences 24 hours before death. Receiver operating characteristic curves for 24-hour mortality demonstrated superior RI performance with c-statistics, 0.82 and 0.93, respectively. At the point where MEWS triggers an alarm, we identified the RI point corresponding to equal sensitivity and found the positive likelihood ratio (LR+) for MEWS was 7.8, and for the RI was 16.9 with false alarms reduced by 53%. At the RI point corresponding to equal LR+, the sensitivity for MEWS was 49% and 77% for RI, capturing 54% more of those patients who will die within 24 hours. Journal of Hospital Medicine 2014;9:116–119. 2013 The Authors. Journal of Hospital Medicine published by Wiley Periodicals, Inc. on behalf of Society of Hospital Medicine

Automated analysis of electronic medical record data reflects the pathophysiology of operative complications

We hypothesized that a novel algorithm that uses data from the electronic medical record (EMR) from multiple clinical and biometric sources could provide early warning of organ dysfunction in patients with high risk for postoperative complications and sepsis. Operative patients undergoing colorectal procedures were evaluated. The Rothman Index (RI) is a predictive model based on heuristic equations derived from 26 variables related to inpatient care. The RI integrates clinical nursing observations, bedside biometrics, and laboratory data into a continuously updated, numeric physiologic assessment, ranging from 100 (unimpaired) to -91. The RI can be displayed within the EMR as a graphic trend, with a decreasing trend reflecting physiologic dysfunction. Patients undergoing colorectal procedures between June and October 2011 were evaluated to determine correlation of initial RI, average inpatient RI, and lowest RI to incidence of complications and/or postoperative sepsis. Patients were stratified by color-coded RI risk group (100-65, blue; 64-40, yellow; <40 red). One-way or repeated-measures analysis of variance was used to compare groups by age, number of complications, and presence of sepsis defined by discharge International Classification of Diseases, 9(th) Revision, codes. Mean direct cost of care and duration of stay also was calculated for each group. The overall incidence of perioperative complications in the 124 patient cohort was 51% (n = 64 patients). The 261 complications sustained by this group represented 82 distinct diagnoses. The 10 patients with sepsis (8%) experienced a 40% mortality. Analysis of initial RI for the population stratified by number of complications and/or sepsis demonstrated a risk-related difference. With progressive onset of complications, the RI decreased, suggesting worsening physiologic dysfunction and linear increase in direct cost of care. These findings demonstrate that EMR data can be automatically compiled into an objective metric that reflects patient risk and changing physiologic state. The automated process of continuous update reflects a physiologic trajectory associated with evolving organ system dysfunction indicative of postoperative complications. Early intervention based on these trends may guide preoperative counseling, enhance pre-emptive management of adverse occurrences, and improve cost-efficiency of care.

Identifying patients at increased risk for unplanned readmission.

Reducing readmissions is a national priority, but many hospitals lack practical tools to identify patients at increased risk of unplanned readmission. To estimate the association between a composite measure of patient condition at discharge, the Rothman Index (RI), and unplanned readmission within 30 days of discharge. Adult medical and surgical patients in a major teaching hospital in 2011. The RI is a composite measure updated regularly from the electronic medical record based on changes in vital signs, nursing assessments, Braden score, cardiac rhythms, and laboratory test results. We developed 4 categories of RI and tested its association with readmission within 30 days, using logistic regression, adjusted for patient age, sex, insurance status, service assignment (medical or surgical), and primary discharge diagnosis. Sixteen percent of the sample patients (N=2730) had an unplanned readmission within 30 days of discharge. The risk of readmission for a patient in the highest risk category (RI<70) was >1 in 5 while the risk of readmission for patients in the lowest risk category was about 1 in 10. In multivariable analysis, patients with an RI<70 (the highest risk category) or 70-79 (medium risk category) had 2.65 (95% confidence interval, 1.72-4.07) and 2.40 (95% confidence interval, 1.57-3.67) times higher odds of unplanned readmission, respectively, compared with patients in the lowest risk category. Clinicians can use the RI to help target hospital programs and supports to patients at highest risk of readmission.

Development and validation of a continuous measure of patient condition using the Electronic Medical Record

Patient condition is a key element in communication between clinicians. However, there is no generally accepted definition of patient condition that is independent of diagnosis and that spans acuity levels. We report the development and validation of a continuous measure of general patient condition that is independent of diagnosis, and that can be used for medical-surgical as well as critical care patients.A survey of Electronic Medical Record data identified common, frequently collected non-static candidate variables as the basis for a general, continuously updated patient condition score. We used a new methodology to estimate in-hospital risk associated with each of these variables. A risk function for each candidate input was computed by comparing the final pre-discharge measurements with 1-year post-discharge mortality. Step-wise logistic regression of the variables against 1-year mortality was used to determine the importance of each variable. The final set of selected variables consisted of 26 clinical measurements from four categories: nursing assessments, vital signs, laboratory results and cardiac rhythms. We then constructed a heuristic model quantifying patient condition (overall risk) by summing the single-variable risks. The model’s validity was assessed against outcomes from 170,000 medical-surgical and critical care patients, using data from three US hospitals.Outcome validation across hospitals yields an area under the receiver operating characteristic curve(AUC) of ⩾0.92when separating hospice/deceased from all other discharge categories, an AUC of ⩾0.93 when predicting 24-h mortalityand an AUC of 0.62 when predicting 30-day readmissions. Correspondence with outcomesreflective of patient condition across the acuity spectrum indicates utility in both medical-surgical unitsand critical care units. The model output, which we call the Rothman Index, may provide clinicians witha longitudinal view of patient condition to help address known challenges in caregiver communication,continuity of care, and earlier detection of acuity trends.

Rothman index as a predictor for type of discharge and readmission rates in a cancer hospital: The Yale experience.

6635 Background: There is limited data on predictors of discharge (DC) and readmission (RA) for oncology inpatients. The Rothman index (RI) is a single score calculated from 26 parameters including nursing assessments, vital signs, and laboratory tests, integrated with the hospital’s electronic medical record and automatically updated when new data are entered. Scores are inversely related to patient impairment. We evaluated the role of RI in the prediction of DC types and RA rates at the Smilow Cancer Hospital at Yale. Methods: Patients (pts) with solid tumors, length of stay longer than 24 hours, admitted between 6/1/11 and 12/31/11 were enrolled. Elective admissions were excluded. DC types were classified as home without hospice (Hm), nursing home (NH), hospice (Hp) and death (D). The RI at the time of DC was evaluated as a predictor for RA at 7 or 30 days and the type of DC. Analysis of variance (ANOVA) was used to determine whether there were statistically significant differences between RI means and DC categories, followed by pair-wise contrast using the Tukey method. Results: Among the 476 admissions from 331 pts, the median age was 63 (range 21-92, 23% age 70 or older), and 55% males. The most common tumors were lung (24%) and breast (8%). The number of DCs were 313 Hm (66%), 75 NH (16%), 55 Hp (11%) and 33 D (7%). RI at DC for H, NH, Hp and D were 74, 62, 47 and 23. All comparisons among RI at DC and type of DC were significant (p &lt; 0.05). Among the Hm pts, the rates of RA at 7 days were significantly higher for RI &lt; 50 (45%) and 50-60 (32%) compared to 81-90 (13%) and &gt; 90 (7%) (Table). RA rates at 30 days were also related to RI at DC, ranging from 65% in RI &lt; 50 to 33% in RI &gt; 90. Conclusions: RI is a useful tool in the evaluation of inpatients by providing an objective metric at the time of DC. The high correlation between RI scores and both DC type and RA rate underscores the need for a more consistent and reproducible evaluation at the time of DC and may be used by health care providers to guide disposition planning. A prospective study is being planned at our institution. [Table: see text]