Placing clinical variables on a common linear scale of empirically based risk as a step towards construction of a general patient acuity score from the electronic health record: a modelling study

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

OBJECTIVES

Triaging patients at admission to determine subsequent deterioration risk can be difficult. This is especially true of coronavirus disease 2019 patients, some of whom experience significant physiologic deterioration due to dysregulated immune response following admission. A well-established acuity measure, the Rothman Index, is evaluated for stratification of patients at admission into high or low risk of subsequent deterioration.

DESIGN

Multicenter retrospective study.

SETTING

One academic medical center in Connecticut, and three community hospitals in Connecticut and Maryland.

PATIENTS

Three thousand four hundred ninety-nine coronavirus disease 2019 and 14,658 noncoronavirus disease 2019 adult patients admitted to a medical service between January 1, 2020, and September 15, 2020.

INTERVENTIONS

None.

MEASUREMENTS AND MAIN RESULTS

Performance of the Rothman Index at admission to predict in-hospital mortality or ICU utilization for both general medical and coronavirus disease 2019 populations was evaluated using the area under the curve. Precision and recall for mortality prediction were calculated, high- and low-risk thresholds were determined, and patients meeting threshold criteria were characterized. The Rothman Index at admission has good to excellent discriminatory performance for in-hospital mortality in the coronavirus disease 2019 (area under the curve, 0.81-0.84) and noncoronavirus disease 2019 (area under the curve, 0.90-0.92) populations. We show that for a given admission acuity, the risk of deterioration for coronavirus disease 2019 patients is significantly higher than for noncoronavirus disease 2019 patients. At admission, Rothman Index-based thresholds segregate the majority of patients into either high- or low-risk groups; high-risk groups have mortality rates of 34-45% (coronavirus disease 2019) and 17-25% (noncoronavirus disease 2019), whereas low-risk groups have mortality rates of 2-5% (coronavirus disease 2019) and 0.2-0.4% (noncoronavirus disease 2019). Similarly large differences in ICU utilization are also found.

CONCLUSIONS

Acuity level at admission may support rapid and effective risk triage. Notably, in-hospital mortality risk associated with a given acuity at admission is significantly higher for coronavirus disease 2019 patients than for noncoronavirus disease 2019 patients. This insight may help physicians more effectively triage coronavirus disease 2019 patients, guiding level of care decisions and resource allocation.

Analysis of Rothman Index Data to Predict Postdischarge Adverse Events in a Medical Intensive Care Unit

OBJECTIVE

Currently, there are no objective metrics included in the intensive care unit (ICU) discharge decision making process. In this study, we evaluate Rothman Index(RI) data for a possible metric as part of a quality improvement project. Our objectives were to determine whether RI could predict adverse events occurring within 72 hours of ICU discharge decision, the optimal clinical cutoff value for this metric, and to determine whether there is a relation between the RI warning alert 24 hours prior to discharge and adverse events postdischarge.

DESIGN

Retrospective observational study.

SETTING

Single center tertiary hospital.

PATIENTS

Adult medical ICU patients discharged from the ICU between January 20, 2015 and March 14, 2015.

INTERVENTIONS

None.

MEASUREMENTS AND MAIN RESULTS

A total of 194 patients were studied with mean age of 62.74 (18.37) years. Data collection included RI at the time of decision-making for ICU discharge and the presence of any warning signals in the previous 24 hours. A 72-hour follow-up chart review recorded any adverse events, including readmission to a higher level of care, discontinuation of discharge due to clinical status change, emergency department visit if discharged home, rapid response activation, or cardiopulmonary arrest postdischarge. Adverse events after ICU discharge were observed in 31 (16%) patients with 9 events being ICU readmission (4.6%). Based on an age-adjusted multivariate model, a higher RI was associated with lower odds of an adverse event (odds ratio [OR] = 0.969, P = .006, confidence interval [CI]: 0.9487-0.9911). An RI value ≥ 50 was associated with 72% lower odds of an adverse event (OR = 0.2887, 95% CI = 0.1278-0.6517 and P = .003) compared to RI < 50. This RI cutoff value was associated with the largest decrease in odds of events. As expected, patients with a very high-risk warning alert had a higher proportion of adverse events compared to patients who did not. (31.75% vs 12.65%, P = < .02).

CONCLUSIONS

Patients who have an RI < 50 or a very high-risk warning alert have a higher risk of adverse events postdischarge from the ICU. Rothman Index may be a useful metric for ICU discharge decision-making.

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.

"Deterioration to Door Time": An Exploratory Analysis of Delays in Escalation of Care for Hospitalized Patients

BACKGROUND

Timely escalation of care for patients experiencing clinical deterioration in the inpatient setting is challenging. Deterioration on a general floor has been associated with an increased risk of death, and the early period of deterioration may represent a time during which admission to the intensive care unit (ICU) improves survival. Previous studies examining the association between delay from onset of clinical deterioration to ICU transfer and mortality are few in number and were conducted more than 10 years ago.

OBJECTIVE

We aimed to evaluate the impact of delays in the escalation of care among clinically deteriorating patients in the current era of inpatient medicine.

DESIGN AND PARTICIPANTS

This was a retrospective cohort study that analyzed data from 793 patients transferred from non-intensive care unit (ICU) inpatient floors to the medical intensive care unit (MICU), from 2011 to 2013 at an urban, tertiary, academic medical center.

MAIN MEASURES

"Deterioration to door time (DTDT)" was defined as the time between onset of clinical deterioration (as evidenced by the presence of one or more vital sign indicators including respiratory rate, systolic blood pressure, and heart rate) and arrival in the MICU.

KEY RESULTS

In our sample, 64.6 % had delays in care escalation, defined as greater than 4 h based on previous studies. Mortality was significantly increased beginning at a DTDT of 12.1 h after adjusting for age, gender, and severity of illness.

CONCLUSIONS

Delays in the escalation of care for clinically deteriorating hospitalized patients remain frequent in the current era of inpatient medicine, and are associated with increased in-hospital mortality. Development of performance measures for the care of clinically deteriorating inpatients remains essential, and timeliness of care escalation deserves further consideration.

Development of a tool for defining and identifying the dying patient in hospital: Criteria for Screening and Triaging to Appropriate aLternative care (CriSTAL)

OBJECTIVE

To develop a screening tool to identify elderly patients at the end of life and quantify the risk of death in hospital or soon after discharge for to minimise prognostic uncertainty and avoid potentially harmful and futile treatments.

DESIGN

Narrative literature review of definitions, tools and measurements that could be combined into a screening tool based on routinely available or obtainable data at the point of care to identify elderly patients who are unavoidably dying at the time of admission or at risk of dying during hospitalisation.

MAIN MEASUREMENTS

Variables and thresholds proposed for the Criteria for Screening and Triaging to Appropriate aLternative care (CriSTAL screening tool) were adopted from existing scales and published research findings showing association with either in-hospital, 30-day or 3-month mortality.

RESULTS

Eighteen predictor instruments and their variants were examined. The final items for the new CriSTAL screening tool included: age ≥65; meeting ≥2 deterioration criteria; an index of frailty with ≥2 criteria; early warning score >4; presence of ≥1 selected comorbidities; nursing home placement; evidence of cognitive impairment; prior emergency hospitalisation or intensive care unit readmission in the past year; abnormal ECG; and proteinuria.

CONCLUSIONS

An unambiguous checklist may assist clinicians in reducing uncertainty patients who are likely to die within the next 3 months and help initiate transparent conversations with families and patients about end-of-life care. Retrospective chart review and prospective validation will be undertaken to optimise the number of prognostic items for easy administration and enhanced generalisability. Development of an evidence-based tool for defining and identifying the dying patient in hospital: CriSTAL.

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.

Shared decision-making at end-of-life is aided by graphical trending of illness severity

The Rothman Index (RI) gives a visual picture of patient's condition and progress for the physician and family to view together. This case demonstrates how the RI graph facilitates physician-family communication. An 85-year-old man with normal pressure hydrocephalus and ventriculoperitoneal shunt presented with a subdural haematoma. He required a temporoparietal craniotomy and evacuation of left subdural haematoma, followed by care in an intensive inpatient rehabilitation unit. His course was complicated by aspiration pneumonia, dehydration, renal failure and phenytoin toxicity. During hospitalisation, the patient's RI graph was reviewed daily with his family. The RI provided an unambiguous visualisation of the trend of patient acuity, which depicted the patient's persistent decline in health, and made clear to the family the situation of the patient. This clarity was instrumental in prompting frank discussions of prognosis and consideration of comfort measures, resulting in timely transfer to hospice.

Identifying patients at increased risk for unplanned readmission

BACKGROUND

Reducing readmissions is a national priority, but many hospitals lack practical tools to identify patients at increased risk of unplanned readmission.

OBJECTIVE

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.

SUBJECTS

Adult medical and surgical patients in a major teaching hospital in 2011.

MEASURES

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.

RESULTS

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.

CONCLUSION

Clinicians can use the RI to help target hospital programs and supports to patients at highest risk of readmission.