Identifying patients at increased risk for unplanned readmission

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.

Community Paramedicine Intervention Reduces Hospital Readmission and Emergency Department Utilization for Patients with Cardiopulmonary Conditions

OBJECTIVE

Patients discharged from the hospital with diagnoses of myocardial infarction, congestive heart failure or acute exacerbation of chronic obstructive pulmonary disease (COPD) have high rates of readmission. We sought to quantify the impact of a community paramedicine (CP) intervention on hospital readmission and emergency department (ED) and clinic utilization for patients discharged with these conditions and to calculate the difference in healthcare costs.

METHODS

This was a prospective, observational cohort study with a matched historical control. The groups were matched for qualifying diagnosis, age, gender, and ZIP code. The intervention group received 1-2 home visits per week by a community paramedic for 30 days. We calculated the number of all-cause hospital readmissions and ED and clinic visits, and used descriptive statistics to compare cohorts.

RESULTS

Included in the study were 78 intervention patients and 78 controls. Compared to controls, fewer subjects in the CP cohort had experienced a readmission at 120 days (34.6% vs 64.1%, P < 0.001) and 210 days (43.6% vs 75.6%, P < 0.001) after discharge. At 210 days the CP cohort had 40.9% fewer total hospital admissions, saving 218 bed days and $410,428 in healthcare costs. The CP cohort had 40.7% fewer total ED visits.

CONCLUSION

Patients who received a post-hospital community paramedic intervention had fewer hospital readmissions and ED visits, which resulted in saving 218 bed days and decreasing healthcare costs by $410,428. Incorporation of a home CP intervention of 30 days in this patient population has the potential to benefit payors, hospitals, and patients.

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

Introduction

The 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).

Methods

Medical 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.

Results

Median 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).

Conclusions

High-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.

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

BACKGROUND

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.

METHODS

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.

RESULTS

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).

CONCLUSION

RI values may be used to predict readmission or home discharge after TKA.

Implementing an Individual-Centric Discharge Process across Singapore Public Hospitals

Singapore is one of the first known countries to implement an individual-centric discharge process across all public hospitals to manage frequent admissions-a perennial challenge for public healthcare, especially in an aging population. Specifically, the process provides daily lists of high-risk patients to all public hospitals for customized discharge procedures within 24 h of admission. We analyzed all public hospital admissions (N = 150,322) in a year. Among four models, the gradient boosting machine performed the best (AUC = 0.79) with a positive predictive value set at 70%. Interestingly, the cumulative length of stay (LOS) in the past 12 months was a stronger predictor than the number of previous admissions, as it is a better proxy for acute care utilization. Another important predictor was the "number of days from previous non-elective admission", which is different from previous studies that included both elective and non-elective admissions. Of note, the model did not include LOS of the index admission-a key predictor in other models-since our predictive model identified frequent admitters for pre-discharge interventions during the index (current) admission. The scientific ingredients that built the model did not guarantee its successful implementation-an "art" that requires the alignment of processes, culture, human capital, and senior management sponsorship. Change management is paramount, otherwise data-driven health policies, no matter how well-intended, may not be accepted or implemented. Overall, our study demonstrated the viability of using artificial intelligence (AI) to build a near real-time nationwide prediction tool for individual-centric discharge, and the critical factors for successful implementation.

Assessment of Potentially Preventable Hospital Readmissions After Major Surgery and Association With Public vs Private Health Insurance and Comorbidities

IMPORTANCE

Rehospitalization after major surgery is common and represents a significant cost to the health care system. Little is known regarding the causes of these readmissions and the degree to which they may be preventable.

OBJECTIVE

To evaluate the degree to which readmissions after major surgery are potentially preventable.

DESIGN, SETTING, AND PARTICIPANTS

This retrospective cohort study used a weighted sample of 1 937 354 patients from the 2017 National Readmissions Database to evaluate all adult inpatient hospitalizations for 1 of 7 common major surgical procedures. Statistical analysis was performed from January 14 to November 30, 2020.

MAIN OUTCOMES AND MEASURES

The study calculated 90-day readmission rates as well as rates of readmissions that were considered potentially preventable. Potentially preventable readmissions (PPRs) were defined as those with a primary diagnosis code for superficial surgical site infection, acute kidney injury, aspiration pneumonitis, or any of the Agency for Healthcare Research and Quality-defined ambulatory care sensitive conditions. Multivariable logistic regression was used to identify factors associated with PPRs.

RESULTS

A total weighted sample of 1 937 354 patients (1 048 046 women [54.1%]; mean age, 66.1 years [95% CI, 66.0-66.3 years]) underwent surgical procedures; 164 755 (8.5%) experienced a readmission within 90 days. Potentially preventable readmissions accounted for 29 321 (17.8%) of all 90-day readmissions, for an estimated total cost to the US health care system of approximately $296 million. The most common reasons for PPRs were congestive heart failure exacerbation (34.6%), pneumonia (12.0%), and acute kidney injury (22.5%). In a multivariable model of adults aged 18 to 64 years, patients with public health insurance (Medicare or Medicaid) had more than twice the odds of PPR compared with those with private insurance (adjusted odds ratio, 2.09; 95% CI, 1.94-2.25). Among patients aged 65 years or older, patients with private insured had 18% lower odds of PPR compared with patients with Medicare as the primary payer (adjusted odds ratio, 0.82; 95% CI, 0.74-0.90).

CONCLUSIONS AND RELEVANCE

This study suggests that nearly 1 in 5 readmissions after surgery are potentially preventable and account for nearly $300 million in costs. In addition to better inpatient care, improved access to ambulatory care may represent an opportunity to reduce costly readmissions among surgical patients.

Implementation and Continuous Monitoring of an Electronic Health Record Embedded Readmissions Clinical Decision Support Tool

Unplanned hospital readmissions represent a significant health care value problem with high costs and poor quality of care. A significant percentage of readmissions could be prevented if clinical inpatient teams were better able to predict which patients were at higher risk for readmission. Many of the current clinical decision support models that predict readmissions are not configured to integrate closely with the electronic health record or alert providers in real-time prior to discharge about a patient's risk for readmission. We report on the implementation and monitoring of the Epic electronic health record-"Unplanned readmission model version 1"-over 2 years from 1/1/2018-12/31/2019. For patients discharged during this time, the predictive capability to discern high risk discharges was reflected in an AUC/C-statistic at our three hospitals of 0.716-0.760 for all patients and 0.676-0.695 for general medicine patients. The model had a positive predictive value ranging from 0.217-0.248 for all patients. We also present our methods in monitoring the model over time for trend changes, as well as common readmissions reduction strategies triggered by the score.

Hemorrhoidectomy for elderly patients aged 75 years or more, before and after studies

Background

The incidence of hemorrhoids requiring hemorrhoidectomy among the elderly has been increasing. Old age is sometimes considered a contraindication for surgery. The relationship between age and complications of hemorrhoidectomy for elderly patients is not well established. This study aimed to compare the clinicopathological features and postoperative outcomes of hemorrhoidectomy in the elderly (≥75 years old) and non-elderly patients (<75 years old).

Methods

A total of 100 patients who underwent hemorrhoidectomy for hemorrhoids of Goligher classification grades 3 and 4 at our institution between 2014 and 2018 were enrolled. The clinical characteristics were compared between the elderly and non-elderly patients. Pain scores were measured at 6, 12, 24, and 48 h after surgery. The risk factors for postoperative complications were identified.

Results

A total of 34 patients were classified as elderly patients. In the elderly group, aspartate aminotransferase levels were higher while the albumin levels and cholinesterase levels were lower and the platelet counts were significantly lower. The blood urea nitrogen levels were higher and estimated glomerular filtration rates and hemoglobin levels were significantly lower in the elderly group. The pain scores significantly decreased at 48 h postoperatively compared to those recorded at 6 h postoperatively in both groups. Multivariate analysis identified Goligher classification grade 4 and high neutrophil to lymphocyte ratio at the indicators of complications.

Conclusions

Hemorrhoids due to impairment of liver function and kidney function were dominant in elderly patients. Aging itself was not a risk factor for postoperative complications.

•

The feasibility of hemorrhoidectomy for the elderly is not remained clear.

•

The incidence of postoperative complications was comparable regardless of age.

•

The risk factor for complication was Goligher classification grade 4 and high NLR.

Application of electronic medical record-derived analytics in critical care: Rothman Index predicts mortality and readmissions in surgical intensive care unit patients

INTRODUCTION

The Rothman Index (RI) is an objective measurement of a patient's overall condition, automatically generated from 26 variables including vital signs, laboratory data, cardiac rhythms, and nursing assessments. The purpose of this study was to assess the validity of RI scores in predicting surgical ICU (SICU) readmission rates and mortality.

METHODS

We conducted a single-center retrospective analysis of surgical patients who were transferred from the SICU to the surgical floor from December 2014 to December 2016. Data included demographics, length of stay (LOS), mortality, and RI at multiple pretransfer and post-transfer time points.

RESULTS

A total of 1,445 SICU patients were transferred to the surgical floor; 79 patients (5.5%) were readmitted within 48 hours of transfer. Mean age was 52 years, and 67% were male. Compared to controls, patients readmitted to the SICU within 48 hours experienced higher LOS (29 vs. 11 days, p < 0.05) as well as higher mortality (2.5% vs. 0.6%, p < 0.05). Patients requiring readmission also had a lower RI at 72, 48, and 24 hours before transfer as well as at 24 and 48 hours after transfer (p < 0.05 for all). Rothman Index scores were categorized into higher-risk (<40), medium-risk (40-65), and lower-risk groups (>65); RI scores at 24 hours before transfer were inversely proportional to overall mortality (RI < 40 = 2.5%, RI 40-65 = 0.3%, and RI > 65 = 0%; p < 0.05) and SICU readmission rates (RI < 40 = 9%, RI 40-65 = 5.2%, and RI > 65 = 2.8%; p < 0.05). Patients transferred with RI scores greater than 83 did not require SICU readmission within 48 hours.

CONCLUSION

Surgical ICU patients requiring readmission within 48 hours of transfer have a significantly higher mortality and longer LOS compared to those who do not. Patients requiring readmission also have significantly lower pretransfer and post-transfer RI scores compared to those who do not. Rothman Index scores may be used as a clinical tool for evaluating patients before transfer from the SICU. Prospective studies are warranted to further validate use of this technology.

LEVEL OF EVIDENCE

Retrospective database review, level III.

Frailty and functional decline after emergency abdominal surgery in the elderly: a prospective cohort study

Background

Frailty has been associated with an increased risk of adverse postoperative outcomes in elderly patients. We examined the impact of preoperative frailty on loss of functional independence following emergency abdominal surgery in the elderly.

Methods

This prospective cohort study was performed at a tertiary hospital, enrolling patients 65 years of age and above who underwent emergency abdominal surgery from June 2016 to February 2018. Premorbid variables, perioperative characteristics and outcomes were collected. Two frailty measures were compared in this study-the Modified Fried's Frailty Criteria (mFFC) and Modified Frailty Index-11 (mFI-11). Patients were followed-up for 1 year.

Results

A total of 109 patients were prospectively recruited. At baseline, 101 (92.7%) were functionally independent, of whom seven (6.9%) had loss of independence at 1 year; 28 (25.7%) and 81 (74.3%) patients were frail and non-frail (by mFFC) respectively. On univariate analysis, age, Charlson Comorbidity Index and frailty (mFFC) (univariate OR 13.00, 95% CI 2.21-76.63, p < 0.01) were significantly associated with loss of functional independence at 1 year. However, frailty, as assessed by mFI-11, showed a weaker correlation than mFFC (univariate OR 4.42, 95% CI 0.84-23.12, p = 0.06). On multivariable analysis, only premorbid frailty (by mFFC) remained statistically significant (OR 15.63, 95% CI 2.12-111.11, p < 0.01).

Conclusions

The mFFC is useful for frailty screening amongst elderly patients undergoing emergency abdominal surgery and is a predictor for loss of functional independence at 1 year. Including the risk of loss of functional independence in perioperative discussions with patients and caregivers is important for patient-centric emergency surgical care. Early recognition of this at-risk group could help with discharge planning and priority for post-discharge support should be considered.

Comparison of Factors Identified by Patients and Physicians Associated with Hospital Readmission (COMPARE2)

OBJECTIVE

Factors contributing to hospital readmission have rarely been sought from the patient perspective. Furthermore, it is unclear how patients and physicians compare in identifying factors contributing to readmission. The objective of the study was to identify and compare factors contributing to hospital readmission identified by patients and physicians by surveying participants upon hospital readmission to a teaching medicine service.

METHODS

Patients 18 years and older who were discharged and readmitted to the same service within 30 days and the physicians caring for these patients were surveyed to identify factors contributing to readmission. Secondary outcomes included comparing responses between groups and determining level of agreement. Patients could be surveyed multiple times on subsequent readmissions; physicians could be surveyed for multiple patients.

RESULTS

A total of 131 patients and 37 physicians were consented. The mean patient age was 60.1 years (standard deviation 16.8 years) and 55.6% were female; 56.4% were white, and 42.1% were black/African American. In total, 179 patient surveys identified "multiple medical problems" (48.6%), "trouble completing daily activities" (45.8%), and "discharged too soon" (43.6%) most frequently as contributing factors; 231 physician surveys identified "multiple medical problems" (45.0%) and "medical condition too difficult to care for at home" (35.6%) most frequently as contributing factors. Paired survey results were available for 135 readmissions and showed fair agreement for only 1 factor but no agreement for 5 factors.

CONCLUSIONS

Patients identified previously unknown factors contributing to readmission. Little agreement existed between patients and physicians. Additional research is needed to determine how best to address patient-identified factors contributing to readmission.

An argument for reporting data standardization procedures in multi-site predictive modeling: case study on the impact of LOINC standardization on model performance

Objectives

We aimed to gain a better understanding of how standardization of laboratory data can impact predictive model performance in multi-site datasets. We hypothesized that standardizing local laboratory codes to logical observation identifiers names and codes (LOINC) would produce predictive models that significantly outperform those learned utilizing local laboratory codes.

Materials and Methods

We predicted 30-day hospital readmission for a set of heart failure-specific visits to 13 hospitals from 2008 to 2012. Laboratory test results were extracted and then manually cleaned and mapped to LOINC. We extracted features to summarize laboratory data for each patient and used a training dataset (2008–2011) to learn models using a variety of feature selection techniques and classifiers. We evaluated our hypothesis by comparing model performance on an independent test dataset (2012).

Results

Models that utilized LOINC performed significantly better than models that utilized local laboratory test codes, regardless of the feature selection technique and classifier approach used.

Discussion and Conclusion

We quantitatively demonstrated the positive impact of standardizing multi-site laboratory data to LOINC prior to use in predictive models. We used our findings to argue for the need for detailed reporting of data standardization procedures in predictive modeling, especially in studies leveraging multi-site datasets extracted from electronic health records.

Inpatient disposition in overcrowded hospitals: is it safe and effective to use reverse triage and readmission screening tools for appropriate discharge? An observational prospective study of an Italian II level hospital

BACKGROUND

Reverse triage (RT) identifies patients eligible for discharge and have been proposed to cope with daily surge. Nevertheless, early discharge could increase the rate of readmission. Our aim is to test the effectiveness and safety of RT alone and with readmission screening tools (Identification Senior At Risk [ISAR], HOSPITAL, and Groeningen Frailty Index [GFI] scores) to predict appropriate discharge.

MATERIAL AND METHODS

We prospectively assessed every 4 days (t0 ) inpatients of medical divisions (High Dependency Unit (HDU), Internal Medicine (IM), and Geriatrics (Ger)) of an Italian Hospital. RT score was calculated for each patient and an RT ≤3 identified those eligible for safe discharge. ISAR, HOSPITAL, and GFI were then applied. We assessed reinstituting of interventions and transferring to an increased level of care unit at 4 days as an ethical proxy of consequential medical events following hypothetical discharge. Date of effective discharge, death, and readmission were measured at 4, 7, 15, and 30 days after the first evaluation.

RESULTS

Twenty-five (9.6%) out of 260 patients in our sample had an RT ≤3. Twenty-four (96%) of them compared with 205 (87%) of the RT >3 group (P = NS) were discharged. Patients with RT ≤3 were discharged significantly earlier (3.5 vs 8 days after t0 [P = 0.0002]). In the RT ≤3 group, all but one patient were alive and healthy at 7, 15, and 30 days. The HOSPITAL score seemed to have the best concordance with RT (84%), in comparison with the ISAR (52%) and the GFI (48%) scores. RT showed a low sensitivity (22%) and high specificity (95%), which was even higher when using RT associated with readmission screening tools.

CONCLUSIONS

Reverse triage proved to be a safe and conservative tool, with high specificity alone and with readmission screening tools. RT correctly identifies patients that will be discharged earlier.

The Rothman Index as a predictor of postdischarge adverse events after elective spine surgery

BACKGROUND CONTEXT

The Rothman Index (RI) is a comprehensive rating of overall patient condition in the hospital setting. It is used at many medical centers and calculated based on vital signs, laboratory values, and nursing assessments in the electronic medical record. Previous research has demonstrated an association with adverse events, readmission, and mortality in other fields, but it has not been investigated in spine surgery.

PURPOSE

The present study aims to determine the potential utility of the RI as a predictor of adverse events after discharge following elective spine surgery.

STUDY DESIGN/SETTING

This retrospective cohort study was carried out at a large academic medical center.

PATIENT SAMPLE

A total of 2,687 patients who underwent elective spine surgery between 2013 and 2016 were included in the present study.

OUTCOME MEASURES

The occurrence of adverse events and readmission after discharge from the hospital, within postoperative day 30, was determined in the present study.

METHODS

Patient characteristics and 30-day perioperative outcomes were characterized, with events being classified as "major adverse events" or "minor adverse events" using standardized criteria. Rothman Index scores from the hospitalization were analyzed and compared for those who did or did not experience adverse events after discharge. The association of lowest and latest scores on adverse events was determined with multivariate regression, controlling for demographics, comorbidities, surgical procedure, and length of stay.

RESULTS

Postdischarge adverse events were experienced by 7.1% of patients. The latest and lowest RI values were significantly inversely correlated with any adverse events, major adverse events, minor adverse events and readmissions after controlling for age, gender, body mass index, American Society of Anesthesiologists (ASA) class, surgical site, and hospital length of stay. Rates of readmission and any adverse event consistently had an inverse correlation with lowest and latest RI scores, with patients at increased risk with lowest score below 65 or latest score below 85.

CONCLUSIONS

The RI is a tool that can be used to predict postdischarge adverse events after elective spine surgery that adds value to commonly used indices such as patient demographics and ASA. It is found that this can help physicians identify high-risk patients before discharge and should be able to better inform clinical decisions.

The Rothman Index Is Associated With Postdischarge Adverse Events After Hip Fracture Surgery in Geriatric Patients

BACKGROUND

The Rothman Index is a comprehensive measure of overall patient status in the inpatient setting already in use at many medical centers. It ranges from 100 (best score) to -91 (worst score) and is calculated based on 26 variables encompassing vital signs, routine laboratory values, and organ system assessments from nursing rounds from the electronic medical record. Past research has shown an association of Rothman Index with complications, readmission, and death in certain populations, but it has not been evaluated in geriatric patients with hip fractures, a potentially vulnerable patient population.

QUESTIONS/PURPOSES

(1) Is there an association between Rothman Index scores and postdischarge adverse events in a population aged 65 years and older with hip fractures? (2) What is the discriminative ability of Rothman Index scores in determining which patients will or will not experience these adverse events? (3) Are there Rothman Index thresholds associated with increased incidence of postdischarge adverse outcomes?

METHODS

One thousand two hundred fourteen patients aged 65 years and older who underwent hip fracture surgery at an academic medical center between 2013 and 2016 were identified. Demographic and comorbidity characteristics were characterized, and 30-day postdischarge adverse events were calculated. The associations between a 10-unit change in Rothman Index scores and postdischarge adverse events, mortality, and readmission were determined. American Society of Anesthesiologists (ASA) class was used as a measure of comorbidity because prior research has shown its performance to be equivalent or superior to that of calculated comorbidity measures in this data set. We assessed the ability of Rothman Index scores to determine which patients experienced adverse events. Finally, Rothman Index thresholds were assessed for an association with increased incidence of postdischarge adverse outcomes.

RESULTS

We found a strong association between Rothman Index scores and postdischarge adverse events (lowest score: odds ratio [OR] = 1.29 [1.18-1.41], p < 0.001; latest score: OR = 1.37 [1.24-1.52], p < 0.001) after controlling for age, sex, body mass index, ASA class, and surgical procedure performed. The discriminative ability of lowest and latest Rothman Index scores was better than those of age, sex, and ASA class for any adverse event (lowest value: area under the curve [AUC] = 0.641; 95% confidence interval [CI], 0.601-0.681; latest value: AUC = 0.640; 95% CI, 0.600-0.680); age (0.534; 95% CI, 0.493-0.575, p < 0.001 for both), male sex (0.552; 95% CI, 0.518-0.585, p = 0.001 for both), and ASA class (0.578; 95% CI, 0.542-0.614; p = 0.004 for lowest Rothman Index, p = 0.006 for latest Rothman Index). There was never a difference when comparing lowest Rothman Index value and latest Rothman Index value for any of the outcomes (Table 5). Patients experienced increased rates of postdischarge adverse events and mortality with a lowest Rothman Index of ≤ 35 (p < 0.05) or latest Rothman Index of ≤ 55 (p < 0.05).

CONCLUSIONS

The Rothman Index provides an objective method of assessing perioperative risk in the setting of hip fracture surgery in patients older than age 65 years and is more accurate than demographic measures or ASA class. Furthermore, there are Rothman Index thresholds that can be used to identify patients at increased risk of complications. Physicians can use this tool to monitor the condition of patients with hip fracture, recognize patients at high risk of adverse events to consider changing their plan of care, and counsel patients and families. Further investigation is needed to determine whether interventions based on Rothman Index values contribute to improved outcomes or value of hip fracture care.

LEVEL OF EVIDENCE

Level II, diagnostic study.

Predicting 7-day, 30-day and 60-day all-cause unplanned readmission: a case study of a Sydney hospital

BACKGROUND

The identification of patients at high risk of unplanned readmission is an important component of discharge planning strategies aimed at preventing unwanted returns to hospital. The aim of this study was to investigate the factors associated with unplanned readmission in a Sydney hospital. We developed and compared validated readmission risk scores using routinely collected hospital data to predict 7-day, 30-day and 60-day all-cause unplanned readmission.

METHODS

A combination of gradient boosted tree algorithms for variable selection and logistic regression models was used to build and validate readmission risk scores using medical records from 62,235 live discharges from a metropolitan hospital in Sydney, Australia.

RESULTS

The scores had good calibration and fair discriminative performance with c-statistic of 0.71 for 7-day and for 30-day readmission, and 0.74 for 60-day. Previous history of healthcare utilization, urgency of the index admission, old age, comorbidities related to cancer, psychosis, and drug-abuse, abnormal pathology results at discharge, and being unmarried and a public patient were found to be important predictors in all models. Unplanned readmissions beyond 7 days were more strongly associated with longer hospital stays and older patients with higher number of comorbidities and higher use of acute care in the past year.

CONCLUSIONS

This study demonstrates similar predictors and performance to previous risk scores of 30-day unplanned readmission. Shorter-term readmissions may have different causal pathways than 30-day readmission, and may, therefore, require different screening tools and interventions. This study also re-iterates the need to include more informative data elements to ensure the appropriateness of these risk scores in clinical practice.

Development of Imminent Mortality Predictor for Advanced Cancer (IMPAC), a Tool to Predict Short-Term Mortality in Hospitalized Patients With Advanced Cancer

PURPOSE

End-of-life care for patients with advanced cancer is aggressive and costly. Oncologists inconsistently estimate life expectancy and address goals of care. Currently available prognostication tools are based on subjective clinical assessment. An objective prognostic tool could help oncologists and patients decide on a realistic plan for end-of-life care. We developed a predictive model (Imminent Mortality Predictor in Advanced Cancer [IMPAC]) for short-term mortality in hospitalized patients with advanced cancer.

METHODS

Electronic health record data from 669 patients with advanced cancer who were discharged from Yale Cancer Center/Smilow Cancer Hospital were extracted. Statistical learning techniques were used to develop a tool to estimate survival probabilities. Patients were randomly split into training (70%) and validation (30%) sets 20 times. We tested the predictive properties of IMPAC for mortality at 30, 60, 90, and 180 days past the day of admission.

RESULTS

For mortality within 90 days at a 40% sensitivity level, IMPAC has close to 60% positive predictive value. Patients estimated to have a greater than 50% chance of death within 90 days had a median survival time of 47 days. Patients estimated to have a less than 50% chance of death had a median survival of 290 days. Area under the receiver operating characteristic curve for IMPAC averaged greater than .70 for all time horizons tested. Estimated potential cost savings per patient was $15,413 (95% CI, $9,162 to $21,665) in 2014 constant dollars.

CONCLUSION

IMPAC, a novel prognostic tool, can generate life expectancy probabilities in real time and support oncologists in counseling patients about end-of-life care. Potentially avoidable costs are significant.

Thirty-Day Readmission Rate and Costs After Percutaneous Coronary Intervention in the United States

Background—

The association of short-term readmissions after percutaneous coronary intervention (PCI) on healthcare costs has not been well studied.

Methods and Results—

The Healthcare Cost and Utilization Project National Readmission Database encompassing 722 US hospitals was used to identify index PCI cases in patients ≥18 years old. Hierarchical regression analyses were used to examine the factors associated with risk of 30-day readmission and higher cumulative costs. We evaluated 206 869 hospitalized patients who survived to discharge after PCI from January through November 2013 and analyzed readmissions over 30 days after discharge. A total of 24 889 patients (12%) were readmitted within 30 days, with rates ranging from 6% to 17% across hospitals. Among the readmitted patients, 13% had PCI, 2% had coronary artery bypass surgery, and 3% died during the readmission. The most common reasons for readmission included nonspecific chest pain/angina (24%) and heart failure (11%). Mean cumulative costs were higher for those with readmissions ($39 634 versus $22 058; P <0.001). The multivariable analyses showed that readmission increased the log 10 cumulative costs by 45% (β: 0.445; P <0.001). There was no significant difference in cumulative costs by the type of insurance.

Conclusions—

In a national sample of inpatient PCI cases, 30-day readmissions were associated with a significant increase in cumulative costs. The majority of readmissions were because of low-risk chest pain that did not require any intervention. Ongoing effort is warranted to recognize and mitigate potentially preventable post-PCI readmissions.

A systematic review of 30-day readmission after cranial neurosurgery

OBJECTIVE

The 30-day readmission rate has emerged as an important marker of the quality of in-hospital care in several fields of medicine. This review aims to summarize available research reporting readmission rates after cranial procedures and to establish an association with demographic, clinical, and system-related factors and clinical outcomes.

METHODS

The authors conducted a systematic review of several databases; a manual search of the Journal of Neurosurgery, Neurosurgery, Acta Neurochirurgica, Canadian Journal of Neurological Sciences; and the cited references of the selected articles. Quality review was performed using the STROBE (Strengthening the Reporting of Observational Studies in Epidemiology) criteria. Findings are reported according to the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines.

RESULTS

A total of 1344 articles published between 1947 and 2015 were identified; 25 were considered potentially eligible, of which 12 met inclusion criteria. The 30-day readmission rates varied from 6.9% to 23.89%. Complications arising during or after neurosurgical procedures were a prime reason for readmission. Race, comorbidities, and longer hospital stay put patients at risk for readmission.

CONCLUSIONS

Although readmission may be an important indicator for good care for the subset of acutely declining patients, neurosurgery should aim to reduce 30-day readmission rates with improved quality of care through systemic changes in the care of neurosurgical patients that promote preventive measures.

Patients Negatively Impacted by Discontinuity of Nursing Care During Acute Hospitalization

BACKGROUND

Continuity of nursing care in hospitals remains poor and not prioritized, and we do not know whether discontinuous nursing care is negatively impacting patient outcomes.

OBJECTIVES

This study aims to examine nursing care discontinuity and its effect on patient clinical condition over the course of acute hospitalization.

RESEARCH DESIGN

Retrospective longitudinal analysis of electronic health records (EHR). Average point-in-time discontinuity was estimated from time of admission to discharge and compared with theoretical predictions for optimal continuity and random nurse assignment. Mixed-effects models estimated within-patient change in clinical condition following a discontinuity.

SUBJECTS

A total of 3892 adult medical-surgical inpatients were admitted to a tertiary academic medical center in the Eastern United States during July 1, 2011 and December 31, 2011.

MEASURES

Exposure: discontinuity of nursing care was measured at each nurse assessment entry into a patient's EHR as assignment of the patient to a nurse with no prior assignment to that patient.

OUTCOME

patient's clinical condition score (Rothman Index) continuously tracked in the EHR.

RESULTS

Discontinuity declined from nearly 100% in the first 24 hours to 70% at 36 hours, and to 50% by the 10th postadmission day. Discontinuity was higher than predicted for optimal continuity, but not random. Each instance of discontinuity lead to a 0.12-0.23 point decline in the Rothman Index score, with more pronounced effects for older and high-mortality risk patients.

CONCLUSIONS

Discontinuity in acute care nurse assignments was high and negatively impacted patient clinical condition. Improved continuity of provider-patient assignment should be advocated to improve patient outcomes in acute care.

Rankings matter: nurse graduates from higher-ranked institutions have higher productivity

Background

Increasing demand for baccalaureate-prepared nurses has led to rapid growth in the number of baccalaureate-granting programs, and to concerns about educational quality and potential effects on productivity of the graduating nursing workforce. We examined the association of individual productivity of a baccalaureate-prepared nurse with the ranking of the degree-granting institution.

Methods

For a sample of 691 nurses from general medical-surgical units at a large magnet urban hospital between 6/1/2011–12/31/2011, we conducted multivariate regression analysis of nurse productivity on the ranking of the degree-granting institution, adjusted for age, hospital tenure, gender, and unit-specific effects. Nurse productivity was coded as “top”/“average”/“bottom” based on a computation of individual nurse value-added to patient outcomes. Ranking of the baccalaureate-granting institution was derived from the US News and World Report Best Colleges Rankings’ categorization of the nurse’s institution as the “first tier” or the “second tier”, with diploma or associate degree as the reference category.

Results

Relative to diploma or associate degree nurses, nurses who had attended first-tier universities had three-times the odds of being in the top productivity category (OR = 3.18, p < 0.001), while second-tier education had a non-significant association with productivity (OR = 1.73, p = 0.11). Being in the bottom productivity category was not associated with having a baccalaureate degree or the quality tier.

Conclusions

The productivity boost from a nursing baccalaureate degree depends on the quality of the educational institution. Recognizing differences in educational outcomes, initiatives to build a baccalaureate-educated nursing workforce should be accompanied by improved access to high-quality educational institutions.

Derivation and Evaluation of a Risk-Scoring Tool to Predict Participant Attrition in a Lifestyle Intervention Project

Participant attrition in clinical trials and community-based interventions is a serious, common, and costly problem. In order to develop a simple predictive scoring system that can quantify the risk of participant attrition in a lifestyle intervention project, we analyzed data from the Special Diabetes Program for Indians Diabetes Prevention Program (SDPI-DP), an evidence-based lifestyle intervention to prevent diabetes in 36 American Indian and Alaska Native communities. SDPI-DP participants were randomly divided into a derivation cohort (n = 1600) and a validation cohort (n = 801). Logistic regressions were used to develop a scoring system from the derivation cohort. The discriminatory power and calibration properties of the system were assessed using the validation cohort. Seven independent factors predicted program attrition: gender, age, household income, comorbidity, chronic pain, site's user population size, and average age of site staff. Six factors predicted long-term attrition: gender, age, marital status, chronic pain, site's user population size, and average age of site staff. Each model exhibited moderate to fair discriminatory power (C statistic in the validation set: 0.70 for program attrition, and 0.66 for long-term attrition) and excellent calibration. The resulting scoring system offers a low-technology approach to identify participants at elevated risk for attrition in future similar behavioral modification intervention projects, which may inform appropriate allocation of retention resources. This approach also serves as a model for other efforts to prevent participant attrition.

Piloting electronic medical record-based early detection of inpatient deterioration in community hospitals

Patients who deteriorate in the hospital outside the intensive care unit (ICU) have higher mortality and morbidity than those admitted directly to the ICU. As more hospitals deploy comprehensive inpatient electronic medical records (EMRs), attempts to support rapid response teams with automated early detection systems are becoming more frequent. We aimed to describe some of the technical and operational challenges involved in the deployment of an early detection system. This 2-hospital pilot, set within an integrated healthcare delivery system with 21 hospitals, had 2 objectives. First, it aimed to demonstrate that severity scores and probability estimates could be provided to hospitalists in real time. Second, it aimed to surface issues that would need to be addressed so that deployment of the early warning system could occur in all remaining hospitals. To achieve these objectives, we first established a rationale for the development of an early detection system through the analysis of risk-adjusted outcomes. We then demonstrated that EMR data could be employed to predict deteriorations. After addressing specific organizational mandates (eg, defining the clinical response to a probability estimate), we instantiated a set of equations into a Java application that transmits scores and probability estimates so that they are visible in a commercially available EMR every 6 hours. The pilot has been successful and deployment to the remaining hospitals has begun. Journal of Hospital Medicine 2016;11:S18-S24. © 2016 Society of Hospital Medicine.

Reasons for unplanned pediatric readmissions at a referral eye center in the Middle East

Unplanned hospital readmissions are of major interest for health care quality control across all medical subspecialties; however, published data for pediatric ophthalmology are lacking. We reviewed the most common reasons for unplanned pediatric readmissions within 30 days of discharge at a referral eye center in the Middle East. Of the top 5 reasons, 3 were related to problems following corneal suturing, often in the context of penetrating keratoplasty, highlighting that children for whom this is done require particular care and follow-up.

Admission Laboratory Results to Enhance Prediction Models of Postdischarge Outcomes in Cardiac Care

Predictive modeling for postdischarge outcomes of inpatient care has been suboptimal. This study evaluated whether admission numerical laboratory data added to administrative models from New York and Minnesota hospitals would enhance the prediction accuracy for 90-day postdischarge deaths without readmission (PD-90) and 90-day readmissions (RA-90) following inpatient care for cardiac patients. Risk-adjustment models for the prediction of PD-90 and RA-90 were designed for acute myocardial infarction, percutaneous cardiac intervention, coronary artery bypass grafting, and congestive heart failure. Models were derived from hospital claims data and were then enhanced with admission laboratory predictive results. Case-level discrimination, goodness of fit, and calibration were used to compare administrative models (ADM) and laboratory predictive models (LAB). LAB models for the prediction of PD-90 were modestly enhanced over ADM, but negligible benefit was seen for RA-90. A consistent predictor of PD-90 and RA-90 was prolonged length of stay outliers from the index hospitalization.

Does Use of a Hospital-wide Readmission Measure Versus Condition-specific Readmission Measures Make a Difference for Hospital Profiling and Payment Penalties?

BACKGROUND

The Centers for Medicare and Medicaid Services (CMS) use public reporting and payment penalties as incentives for hospitals to reduce readmission rates. In contrast to the current condition-specific readmission measures, CMS recently developed an all-condition, 30-day all-cause hospital-wide readmission measure (HWR) to provide a more comprehensive view of hospital performance.

OBJECTIVES

We examined whether assessment of hospital performance and payment penalties depends on the readmission measure used.

RESEARCH DESIGN

We used inpatient data to examine readmissions for patients discharged from VA acute-care hospitals from Fiscal Years 2007-2010. We calculated risk-standardized 30-day readmission rates for 3 condition-specific measures (heart failure, acute myocardial infarction, and pneumonia) and the HWR measure, and examined agreement between the HWR measure and each of the condition-specific measures on hospital performance. We also assessed the effect of using different readmission measures on hospitals' payment penalties.

RESULTS

We found poor agreement between the condition-specific measures and the HWR measure on those hospitals identified as low or high performers (eg, among those hospitals classified as poor performers by the heart failure readmission measure, only 28.6% were similarly classified by the HWR measure). We also found differences in whether a hospital would experience payment penalties. The HWR measure penalized only 60% of those hospitals that would have received penalties based on at least 1 of the condition-specific measures.

CONCLUSIONS

The condition-specific measures and the HWR measure provide a different picture of hospital performance. Future research is needed to determine which measure aligns best with CMS's overall goals to reduce hospital readmissions and improve quality.

Semi-Competing Risks Data Analysis: Accounting for Death as a Competing Risk When the Outcome of Interest Is Nonterminal

Hospital readmission is a key marker of quality of health care. Notwithstanding its widespread use, however, it remains controversial in part because statistical methods used to analyze readmission, primarily logistic regression and related models, may not appropriately account for patients who die before experiencing a readmission event within the time frame of interest. Toward resolving this, we describe and illustrate the semi-competing risks framework, which refers to the general setting where scientific interest lies with some nonterminal event (eg, readmission), the occurrence of which is subject to a terminal event (eg, death). Although several statistical analysis methods have been proposed for semi-competing risks data, we describe in detail the use of illness-death models primarily because of their relation to well-known methods for survival analysis and the availability of software. We also describe and consider in detail several existing approaches that could, in principle, be used to analyze semi-competing risks data, including composite end point and competing risks analyses. Throughout we illustrate the ideas and methods using data on N=49 763 Medicare beneficiaries hospitalized between 2011 and 2013 with a principle discharge diagnosis of heart failure.

The Frequency of Unplanned Rehospitalization and Associated Factors in Gyneoncology Patients: A Retrospective Study

Objective

In this study, we aim to analyze rate and associated factors with unplanned rehospitalization in gynecological cancer patients.

Materials and Methods

The electronic database query (2007 to 2014) was used to evaluate rehospitalization rates within 90 days of index admission in patients with gynecological cancer. Multivariable logistic regression was used to identify factors associated with rehospitalization.

Results

Mean patient age was 59.05 ± 11.96 years (minimum, 32 years; maximum, 85 years). A total of 152 patients’ data were evaluated. Seventy-three patients (48.0%) were rehospitalized within 90 days of discharge. The median length of index hospital stay (from 3 to 34 days) was 8.90 ± 6.03 days. The most common rehospitalization causes includes pain (24.6%), recurrence (21.9%), ascites (13.7%), surgical site infection (12.3%), acute reoperation (9.6%), thromboembolism (8.2%), renal failure (5.5%), ileus/obstruction (2.7%), and lymphedema (1.4%). In multivariable logistic regression model, difference was found between history of operation, receive chemotherapy, development of the complication during hospitalization comorbidities as well as multiparity variables, and rehospitalization (P < 0.05).

Conclusions

Unplanned rehospitalization after discharge for gynecological cancer is common with significant associated risk factors and patient outcomes. Integrated multidisciplinary health care strategies, such as safe transition, communication, patient and family education, accurate medication reconciliation, and short-interval outpatient follow-up may help to prevent rehospitalization after discharge and improve patient outcomes.

Nonelective Rehospitalizations and Postdischarge Mortality: Predictive Models Suitable for Use in Real Time

BACKGROUND

Hospital discharge planning has been hampered by the lack of predictive models.

OBJECTIVE

To develop predictive models for nonelective rehospitalization and postdischarge mortality suitable for use in commercially available electronic medical records (EMRs).

DESIGN

Retrospective cohort study using split validation.

SETTING

Integrated health care delivery system serving 3.9 million members.

PARTICIPANTS

A total of 360,036 surviving adults who experienced 609,393 overnight hospitalizations at 21 hospitals between June 1, 2010 and December 31, 2013.

MAIN OUTCOME MEASURE

A composite outcome (nonelective rehospitalization and/or death within 7 or 30 days of discharge).

RESULTS

Nonelective rehospitalization rates at 7 and 30 days were 5.8% and 12.4%; mortality rates were 1.3% and 3.7%; and composite outcome rates were 6.3% and 14.9%, respectively. Using data from a comprehensive EMR, we developed 4 models that can generate risk estimates for risk of the combined outcome within 7 or 30 days, either at the time of admission or at 8 AM on the day of discharge. The best was the 30-day discharge day model, which had a c-statistic of 0.756 (95% confidence interval, 0.754-0.756) and a Nagelkerke pseudo-R of 0.174 (0.171-0.178) in the validation dataset. The most important predictors-a composite acute physiology score and end of life care directives-accounted for 54% of the predictive ability of the 30-day model. Incorporation of diagnoses (not reliably available for real-time use) did not improve model performance.

CONCLUSIONS

It is possible to develop robust predictive models, suitable for use in real time with commercially available EMRs, for nonelective rehospitalization and postdischarge mortality.

Development of the Andalusian Registry of Patients Receiving Community Case Management, for the follow-up of people with complex chronic diseases

RATIONALE, AIMS AND OBJECTIVES

Complex chronic diseases are a challenge for the current configuration of health services. Case management is a service frequently provided for people with chronic conditions, and despite its effectiveness in many outcomes, such as mortality or readmissions, uncertainty remains about the most effective form of team organization, structures and the nature of the interventions. Many processes and outcomes of case management for people with complex chronic conditions cannot be addressed with the information provided by electronic clinical records. Registries are frequently used to deal with this weakness. The aim of this study was to generate a registry-based information system of patients receiving case management to identify their clinical characteristics, their context of care, events identified during their follow-up, interventions developed by case managers and services used.

METHODS

The study was divided into three phases, covering the detection of information needs, the design and its implementation in the health care system, using literature review and expert consensus methods to select variables that would be included in the registry.

RESULTS

A total of 102 variables representing structure, processes and outcomes of case management were selected for their inclusion in the registry after the consensus phase. A web-based registry with modular and layered architecture was designed. The framework follows a pattern based on the model-view-controller approach. In its first 6 months after the implementation, 102 case managers have introduced an average number of 6.49 patients each one.

CONCLUSIONS

The registry permits a complete and in-depth analysis of the characteristics of the patients who receive case management, the interventions delivered and some major outcomes as mortality, readmissions or adverse events.

Using what you get: dynamic physiologic signatures of critical illness

The development and resolution of cardiopulmonary instability take time to become clinically apparent, and the treatments provided take time to have an impact. The characterization of dynamic changes in hemodynamic and metabolic variables is implicit in physiologic signatures. When primary variables are collected with high enough frequency to derive new variables, this data hierarchy can be used to develop physiologic signatures. The creation of physiologic signatures requires no new information; additional knowledge is extracted from data that already exist. It is possible to create physiologic signatures for each stage in the process of clinical decompensation and recovery to improve outcomes.

Predicting 30-day readmissions with preadmission electronic health record data

BACKGROUND

Readmission prevention should begin as early as possible during the index admission. Early identification may help target patients for within-hospital readmission prevention interventions.

OBJECTIVES

To develop and validate a 30-day readmission prediction model using data from electronic health records available before the index admission.

RESEARCH DESIGN

Retrospective cohort study of admissions between January 1 and March 31, 2010.

SUBJECTS

Adult enrollees of Clalit Health Services, an integrated delivery system, admitted to an internal medicine ward in any hospital in Israel.

MEASURES

All-cause 30-day emergency readmissions. A prediction score based on before admission electronic health record and administrative data (the Preadmission Readmission Detection Model-PREADM) was developed using a preprocessing variable selection step with decision trees and neural network algorithms. Admissions with a recent prior hospitalization were excluded and automatically flagged as "high-risk." Selected variables were entered into multivariable logistic regression, with a derivation (two-thirds) and a validation cohort (one-third).

RESULTS

The derivation dataset comprised 17,334 admissions, of which 2913 (16.8%) resulted in a 30-day readmission. The PREADM includes 11 variables: chronic conditions, prior health services use, body mass index, and geographical location. The c-statistic was 0.70 in the derivation set and of 0.69 in the validation set. Adding length of stay did not change the discriminatory power of the model.

CONCLUSIONS

The PREADM is designed for use by health plans for early high-risk case identification, presenting discriminatory power better than or similar to that of previously reported models, most of which include data available only upon discharge.

Reflective Practice: A Tool for Readmission Reduction

Factors intrinsic to local practice, but not captured by the medical record, contribute to readmissions. Frontline providers familiar with their practice systems can identify these. The objective was to decrease 30-day hospital readmissions. The intervention involved retrospective review by hospitalists of their own patients' readmissions, using reflective practice guided by a chart review tool. Subjects were patients discharged by hospitalists and readmitted to a tertiary care academic medical center. Hospitalists reviewed 193 readmissions of 170 patients. Factors contributing to readmission were grouped under patient characteristics, operational factors, and care transition. After reflection, physicians scheduled earlier follow-up appointments while nurse practitioners and physician assistants improved discharge instructions. Readmissions decreased during the review period, and the decrease sustained for one year after the review period. Hospitalists reflected on and identified local practice factors that contributed to their own patients' 30-day readmissions. Reflective practice may be an effective strategy to decrease hospital readmissions.

Nurse value-added and patient outcomes in acute care

OBJECTIVE

The aims of the study were to (1) estimate the relative nurse effectiveness, or individual nurse value-added (NVA), to patients' clinical condition change during hospitalization; (2) examine nurse characteristics contributing to NVA; and (3) estimate the contribution of value-added nursing care to patient outcomes.

DATA SOURCES/STUDY SETTING

Electronic data on 1,203 staff nurses matched with 7,318 adult medical-surgical patients discharged between July 1, 2011 and December 31, 2011 from an urban Magnet-designated, 854-bed teaching hospital.

STUDY DESIGN

Retrospective observational longitudinal analysis using a covariate-adjustment value-added model with nurse fixed effects.

DATA COLLECTION/EXTRACTION METHODS

Data were extracted from the study hospital's electronic patient records and human resources databases.

PRINCIPAL FINDINGS

Nurse effects were jointly significant and explained 7.9 percent of variance in patient clinical condition change during hospitalization. NVA was positively associated with having a baccalaureate degree or higher (0.55, p = .04) and expertise level (0.66, p = .03). NVA contributed to patient outcomes of shorter length of stay and lower costs.

CONCLUSIONS

Nurses differ in their value-added to patient outcomes. The ability to measure individual nurse relative value-added opens the possibility for development of performance metrics, performance-based rankings, and merit-based salary schemes to improve patient outcomes and reduce costs.

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

BACKGROUND

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.

METHODS

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.

RESULTS

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.

CONCLUSION

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.

LEVEL OF EVIDENCE

Epidemiologic/prognostic study, level III.

The effects of data sources, cohort selection, and outcome definition on a predictive model of risk of thirty-day hospital readmissions

BACKGROUND

Hospital readmission risk prediction remains a motivated area of investigation and operations in light of the hospital readmissions reduction program through CMS. Multiple models of risk have been reported with variable discriminatory performances, and it remains unclear how design factors affect performance.

OBJECTIVES

To study the effects of varying three factors of model development in the prediction of risk based on health record data: (1) reason for readmission (primary readmission diagnosis); (2) available data and data types (e.g. visit history, laboratory results, etc); (3) cohort selection.

METHODS

Regularized regression (LASSO) to generate predictions of readmissions risk using prevalence sampling. Support Vector Machine (SVM) used for comparison in cohort selection testing. Calibration by model refitting to outcome prevalence.

RESULTS

Predicting readmission risk across multiple reasons for readmission resulted in ROC areas ranging from 0.92 for readmission for congestive heart failure to 0.71 for syncope and 0.68 for all-cause readmission. Visit history and laboratory tests contributed the most predictive value; contributions varied by readmission diagnosis. Cohort definition affected performance for both parametric and nonparametric algorithms. Compared to all patients, limiting the cohort to patients whose index admission and readmission diagnoses matched resulted in a decrease in average ROC from 0.78 to 0.55 (difference in ROC 0.23, p value 0.01). Calibration plots demonstrate good calibration with low mean squared error.

CONCLUSION

Targeting reason for readmission in risk prediction impacted discriminatory performance. In general, laboratory data and visit history data contributed the most to prediction; data source contributions varied by reason for readmission. Cohort selection had a large impact on model performance, and these results demonstrate the difficulty of comparing results across different studies of predictive risk modeling.

"Big data" and the electronic health record

OBJECTIVES

Implementation of Electronic Health Record (EHR) systems continues to expand. The massive number of patient encounters results in high amounts of stored data. Transforming clinical data into knowledge to improve patient care has been the goal of biomedical informatics professionals for many decades, and this work is now increasingly recognized outside our field. In reviewing the literature for the past three years, we focus on "big data" in the context of EHR systems and we report on some examples of how secondary use of data has been put into practice.

METHODS

We searched PubMed database for articles from January 1, 2011 to November 1, 2013. We initiated the search with keywords related to "big data" and EHR. We identified relevant articles and additional keywords from the retrieved articles were added. Based on the new keywords, more articles were retrieved and we manually narrowed down the set utilizing predefined inclusion and exclusion criteria.

RESULTS

Our final review includes articles categorized into the themes of data mining (pharmacovigilance, phenotyping, natural language processing), data application and integration (clinical decision support, personal monitoring, social media), and privacy and security.

CONCLUSION

The increasing adoption of EHR systems worldwide makes it possible to capture large amounts of clinical data. There is an increasing number of articles addressing the theme of "big data", and the concepts associated with these articles vary. The next step is to transform healthcare big data into actionable knowledge.