Prediction Model for Hospital-Acquired Pressure Ulcer Development: Retrospective Cohort Study

Background

A pressure ulcer is injury to the skin or underlying tissue, caused by pressure, friction, and moisture. Hospital-acquired pressure ulcers (HAPUs) may not only result in additional length of hospital stay and associated care costs but also lead to undesirable patient outcomes. Intensive care unit (ICU) patients show higher risk for HAPU development than general patients. We hypothesize that the care team’s decisions relative to HAPU risk assessment and prevention may be better supported by a data-driven, ICU-specific prediction model.

Objective

The aim of this study was to determine whether multiple logistic regression with ICU-specific predictor variables was suitable for ICU HAPU prediction and to compare the performance of the model with the Braden scale on this specific population.

Methods

We conducted a retrospective cohort study by using the data retrieved from the enterprise data warehouse of an academic medical center. Bivariate analyses were performed to compare the HAPU and non-HAPU groups. Multiple logistic regression was used to develop a prediction model with significant predictor variables from the bivariate analyses. Sensitivity, specificity, positive predictive values, negative predictive values, area under the receiver operating characteristic curve (AUC), and Youden index were used to compare with the Braden scale.

Results

The total number of patient encounters studied was 12,654. The number of patients who developed an HAPU during their ICU stay was 735 (5.81% of the incidence rate). Age, gender, weight, diabetes, vasopressor, isolation, endotracheal tube, ventilator episode, Braden score, and ventilator days were significantly associated with HAPU. The overall accuracy of the model was 91.7%, and the AUC was .737. The sensitivity, specificity, positive predictive value, negative predictive value, and Youden index were .650, .693, .211, 956, and .342, respectively. Male patients were 1.5 times more, patients with diabetes were 1.5 times more, and patients under isolation were 3.1 times more likely to have an HAPU than female patients, patients without diabetes, and patients not under isolation, respectively.

Conclusions

Using an extremely large, electronic health record–derived dataset enabled us to compare characteristics of patients who develop an HAPU during their ICU stay with those who did not, and it also enabled us to develop a prediction model from the empirical data. The model showed acceptable performance compared with the Braden scale. The model may assist with clinicians’ decision on risk assessment, in addition to the Braden scale, as it is not difficult to interpret and apply to clinical practice. This approach may support avoidable reductions in HAPU incidence in intensive care.

Novel Visualization of Clostridium difficile Infections in Intensive Care Units

BACKGROUND

Accurate and timely surveillance and diagnosis of healthcare-facility onset Clostridium difficile infection (HO-CDI) is vital to controlling infections within the hospital, but there are limited tools to assist with timely outbreak investigations.

OBJECTIVES

To integrate spatiotemporal factors with HO-CDI cases and develop a map-based dashboard to support infection preventionists (IPs) in performing surveillance and outbreak investigations for HO-CDI.

METHODS

Clinical laboratory results and Admit-Transfer-Discharge data for admitted patients over two years were extracted from the Information Warehouse of a large academic medical center and processed according to Center for Disease Control (CDC) National Healthcare Safety Network (NHSN) definitions to classify Clostridium difficile infection (CDI) cases by onset date. Results were validated against the internal infection surveillance database maintained by IPs in Clinical Epidemiology of this Academic Medical Center (AMC). Hospital floor plans were combined with HO-CDI case data, to create a dashboard of intensive care units. Usability testing was performed with a think-aloud session and a survey.

RESULTS

The simple classification algorithm identified all 265 HO-CDI cases from 1/1/15-11/30/15 with a positive predictive value (PPV) of 96.3%. When applied to data from 2014, the PPV was 94.6% All users "strongly agreed" that the dashboard would be a positive addition to Clinical Epidemiology and would enable them to present Hospital Acquired Infection (HAI) information to others more efficiently.

CONCLUSIONS

The CDI dashboard demonstrates the feasibility of mapping clinical data to hospital patient care units for more efficient surveillance and potential outbreak investigations.

Compliance with preoperative care measures reduces surgical site infection after colorectal operation

BACKGROUND

Surgical site infections (SSIs) are a major cause of morbidity complicating colorectal operations. Several evidence-based preoperative strategies are associated with decreased SSI rates. We hypothesize that compliance with multiple strategies is associated with lower incidence of SSI after the elective colorectal operation.

METHODS

Preoperative care measure compliance before colorectal operations were assessed. Measures included antiseptic wash the night before and day of operation, oral antibiotic, and mechanical bowel preparation, antibiotic prophylaxis, Chloraprep skin preparation, and hair clipping. Rates of SSI after colectomy and other pertinent outcomes were stratified by full and partial compliance with preoperative measures. Exclusion criteria included bowel perforation, ischemia, complete obstruction, intra-abdominal abscess, and no intraoperative skin closure.

RESULTS

Eight hundred twenty-six subjects underwent colectomy between 2010 and 2016; 469 met inclusion criteria. Compliance with all measures occurred in 214 (46%) and was independently associated with lower postoperative infection rates (odds ratio [OR], 0.37; confidence interval [CI], 0.16-0.85; P = 0.02). SSI occurred in 51 (11%): was superficial in 35 (7%); deep in 5 (1%); and organ space in 11 (2%). SSI rates were reduced from 16% (partial or no compliance group) to 5% (full compliance group). No stand-alone intervention was independently associated with decreased SSI rate. Multivariate analysis found the following factors associated with a lower risk of SSI: full compliance with all five process measures, lower BMI, nonsmoker, and minimally invasive operation.

DISCUSSION AND CONCLUSION

Compliance with preoperative care strategies reduces rates of SSI after colectomy with a cumulative effect more pronounced than any single intervention reinforcing the need for protocol-driven and evidence-based care for patients undergoing colorectal operations.

Changes in the burnout profile of chairs of academic departments of obstetrics and gynecology over the past 15 years

BACKGROUND

While many studies have documented the high prevalence of burnout in practicing physicians and medical trainees, fewer reports describe burnout in academic leaders. In 2002, we observed a moderate-high to high level of burnout in 41.4% of chairs of academic departments of obstetrics and gynecology.

OBJECTIVE

The purpose of this study was to identify trends in burnout and associated factors in today's obstetrics and gynecology chairs as they face complex changes to the current health care environment.

STUDY DESIGN

This was a cross-sectional study. A survey was developed based on the questionnaire used in our first investigation and sent electronically to all members of the Council of University Chairs of Obstetrics and Gynecology. Burnout was measured using an abbreviated Maslach Burnout Inventory-Human Sciences Survey. In addition to demographic data, we assessed perceived stressors, job satisfaction, spousal/partner support, self-efficacy, depression, suicidal ideation, and stress management.

RESULTS

The response rate was 60% (84/139). Almost 30% of chairs were women, increased from 7.6% in 2002. Hospital and department budget deficits and loss of key faculty remain major stressors noted by participants. The Maslach Burnout Inventory results have changed dramatically over the past 15 years. Today's chairs demonstrated less burnout but with an "ineffective" profile. Subscale scores for emotional exhaustion and depersonalization were reduced but >50% reported low personal accomplishment. Spousal support remained important in preventing burnout.

CONCLUSION

Chairs of academic departments of obstetrics and gynecology continue to face significant job-related stress. Burnout has decreased; however, personal accomplishment scores have also declined most likely due to administrative factors that are beyond the chairs' perceived control.

HOW NURSES IDENTIFY HOSPITALIZED PATIENTS ON THEIR PERSONAL NOTES: FINDINGS FROM ANALYZING 'BRAINS' HEADERS WITH MULTIPLE RATERS

The overarching objective of this research is to reduce the burden of documentation in electronic health records by registered nurses in hospitals. Registered nurses have consistently reported that e-documentation is a concern with the introduction of electronic health records. As a result, many nurses use handwritten notes in order to avoid using electronic health records to access information about patients. At the top of these notes are patient identifiers. By identifying aspects of good and suboptimal headers, we can begin to form a model of how to effectively support identifying patients during assessments and care activities. The primary finding is that nurses use room number as the primary patient identifier in the hospital setting, not the patient's last name. In addition, the last name, gender, and age are sufficiently important identifiers that they are frequently recorded at the top of handwritten notes. Clearly distinguishable field labels and values are helpful in quickly scanning the identifier for identifying information. A web based annotator was designed as a first step towards machine learning approaches to recognize handwritten or printed data on paper sheets in future research.

IDEA4PS: The Development of a Research-Oriented Learning Healthcare System

Leveraging opportunities to learn and then improve the delivery of care using experiences throughout the health care system is essential in efforts to transform health care delivery. The authors present the approach of one academic medical center in becoming a research-oriented Learning Healthcare System (ro-LHS). By reframing the role of research in improving outcomes, the organization was able to move beyond its focus on quality improvement to foster a culture in which feedback informs practice and research drives improvement. The patient safety learning laboratory, the Institute for the Design of Environments Aligned for Patient Safety, funded by the Agency for Healthcare Research and Quality, has provided foundational infrastructure to connect stakeholders across the medical center and university and conduct rigorous research in the context of practice. With this new focus, research now informs operations in a cycle of continuous improvement across the authors' ro-LHS.

CLUSTERING AND PRIORITIZING PATIENT SAFETY ISSUES DURING EHR IMPLEMENTATION AND UPGRADES IN HOSPITAL SETTINGS

Our aim was to elicit, label, and prioritize clusters of de-identified patient safety issues experienced during the implementation and upgrade installations of Electronic Health Records (EHRs) in hospitals. Conference participants included clinical personnel (physicians, nurses, pharmacists), human factors experts, patient safety experts, information technology experts from vendors and hospitals, academic experts, graduate students, and other attendees. De-identified reports of patient safety issues were shared via share4safety@gmail.com by conference and non-conference attendees before and during a 90-minute session featured at the Human Factors in Healthcare International Symposium on March 8, 2017. One submitted example of a reported patient safety issue was provided to the group. During the session, each attendee shared with a partner five concerns and identified their top concern. Subsequently, each two-person group shared with the larger group these issues, which were written by a facilitator onto sticky paper and placed on the walls. The issues were grouped using pre-defined categories and new categories were identified. Next, each participant voted for the highest priority cluster and/or individual patient safety issue using stickers. This paper reports the results of the interactive session, including the labeled and prioritized clusters and illustrative examples for each cluster. These clusters may inform reporting systems and quality improvement initiatives with health information technology where choices made during implementation and upgrades as well as design flaws with EHR technology both contribute and interact to produce potential patient safety issues.

Reputation and the Best Hospital Rankings: What Does It Really Mean?

U.S. News & World Report’s annual Best Hospital Rankings are determined by an index of quality based on objective and subjective measures. This study investigates relationships between U.S. News score and its individual subjective and objective components that ultimately determine the relative ranking of the top 50 hospitals in the 2015 Best Hospital Rankings for the study’s selected specialties—cancer, cardiology, gastroenterology, neurology, and orthopedics. A 2-step linear regression model was employed; first, to control for objective components’ influence on U.S. News score ( R2 = 0.365; P < .001), then, to isolate the subjective component of reputation’s effect on U.S. News score ( R2 = 0.565; P < .001). The second model confirmed that reputation has a more significant influence on total U.S. News score than its objective counterparts. This indicates that a hospital’s U.S. News score and relative ranking in the Best Hospital Rankings may be disproportionately influenced by its reputation.

Body mass index and pressure ulcers: improved predictability of pressure ulcers in intensive care patients

BACKGROUND

Obesity contributes to immobility and subsequent pressure on skin surfaces. Knowledge of the relationship between obesity and development of pressure ulcers in intensive care patients will provide better understanding of which patients are at high risk for pressure ulcers and allow more efficient prevention.

OBJECTIVES

To examine the incidence of pressure ulcers in patients who differ in body mass index and to determine whether inclusion of body mass index enhanced use of the Braden scale in the prediction of pressure ulcers.

METHODS

In this retrospective cohort study, data were collected from the medical records of 4 groups of patients with different body mass index values: underweight, normal weight, obese, and extremely obese. Data included patients' demographics, body weight, score on the Braden scale, and occurrence of pressure ulcers.

RESULTS

The incidence of pressure ulcers in the underweight, normal weight, obese, and extremely obese groups was 8.6%, 5.5%, 2.8%, and 9.9%, respectively. When both the score on the Braden scale and the body mass index were predictive of pressure ulcers, extremely obese patients were about 2 times more likely to experience an ulcer than were normal weight patients. In the final model, the area under the curve was 0.71. The baseline area under the curve for the Braden scale was 0.68.

CONCLUSIONS

Body mass index and incidence of pressure ulcers were related in intensive care patients. Addition of body mass index did not appreciably improve the accuracy of the Braden scale for predicting pressure ulcers.

Predictive validity of the Braden scale for patients in intensive care units

BACKGROUND

Patients in intensive care units are at higher risk for development of pressure ulcers than other patients. In order to prevent pressure ulcers from developing in intensive care patients, risk for development of pressure ulcers must be assessed accurately.

OBJECTIVES

To evaluate the predictive validity of the Braden scale for assessing risk for development of pressure ulcers in intensive care patients by using 4 years of data from electronic health records. Methods Data from the electronic health records of patients admitted to intensive care units between January 1, 2007, and December 31, 2010, were extracted from the data warehouse of an academic medical center. Predictive validity was measured by using sensitivity, specificity, positive predictive value, and negative predictive value. The receiver operating characteristic curve was generated, and the area under the curve was reported.

RESULTS

A total of 7790 intensive care patients were included in the analysis. A cutoff score of 16 on the Braden scale had a sensitivity of 0.954, specificity of 0.207, positive predictive value of 0.114, and negative predictive value of 0.977. The area under the curve was 0.672 (95% CI, 0.663-0.683). The optimal cutoff for intensive care patients, determined from the receiver operating characteristic curve, was 13.

CONCLUSIONS

The Braden scale shows insufficient predictive validity and poor accuracy in discriminating intensive care patients at risk of pressure ulcers developing. The Braden scale may not sufficiently reflect characteristics of intensive care patients. Further research is needed to determine which possibly predictive factors are specific to intensive care units in order to increase the usefulness of the Braden scale for predicting pressure ulcers in intensive care patients.

Effect of barcode-assisted medication administration on emergency department medication errors

OBJECTIVES

Barcode-assisted medication administration (BCMA) is technology with demonstrated benefit in reducing medication administration errors in hospitalized patients; however, it is not routinely used in emergency departments (EDs). EDs may benefit from BCMA, because ED medication administration is complex and error-prone.

METHODS

A naïve observational study was conducted at an academic medical center implementing BCMA in the ED. The rate of medication administration errors was measured before and after implementing an integrated electronic medical record (EMR) with BCMA capacity. Errors were classified as wrong drug, wrong dose, wrong route of administration, or a medication administration with no physician order. The error type, severity of error, and medications associated with errors were also quantified.

RESULTS

A total of 1,978 medication administrations were observed (996 pre-BCMA and 982 post-BCMA). The baseline medication administration error rate was 6.3%, with wrong dose errors representing 66.7% of observed errors. BCMA was associated with a reduction in the medication administration error rate to 1.2%, a relative rate reduction of 80.7% (p < 0.0001). Wrong dose errors decreased by 90.4% (p < 0.0001), and medication administrations with no physician order decreased by 72.4% (p = 0.057). Most errors discovered were of minor severity. Antihistamine medications were associated with the highest error rate.

CONCLUSIONS

Implementing BCMA in the ED was associated with significant reductions in the medication administration error rate and specifically wrong dose errors. The results of this study suggest a benefit of BCMA on reducing medication administration errors in the ED.