Identifying antinuclear antibody positive individuals at risk for developing systemic autoimmune disease: development and validation of a real-time risk model.

Objective: Positive antinuclear antibodies (ANAs) cause diagnostic dilemmas for clinicians. Currently, no tools exist to help clinicians interpret the significance of a positive ANA in individuals without diagnosed autoimmune diseases. We developed and validated a risk model to predict risk of developing autoimmune disease in positive ANA individuals. Methods: Using a de-identified electronic health record (EHR), we randomly chart reviewed 2,000 positive ANA individuals to determine if a systemic autoimmune disease was diagnosed by a rheumatologist. A priori, we considered demographics, billing codes for autoimmune disease-related symptoms, and laboratory values as variables for the risk model. We performed logistic regression and machine learning models using training and validation samples. Results: We assembled training (n = 1030) and validation (n = 449) sets. Positive ANA individuals who were younger, female, had a higher titer ANA, higher platelet count, disease-specific autoantibodies, and more billing codes related to symptoms of autoimmune diseases were all more likely to develop autoimmune diseases. The most important variables included having a disease-specific autoantibody, number of billing codes for autoimmune disease-related symptoms, and platelet count. In the logistic regression model, AUC was 0.83 (95% CI 0.79-0.86) in the training set and 0.75 (95% CI 0.68-0.81) in the validation set. Conclusion: We developed and validated a risk model that predicts risk for developing systemic autoimmune diseases and can be deployed easily within the EHR. The model can risk stratify positive ANA individuals to ensure high-risk individuals receive urgent rheumatology referrals while reassuring low-risk individuals and reducing unnecessary referrals.

An Effective Program to Reduce Malpractice Claims and Payments in a Large Orthopaedic Practice.

BACKGROUND: High reliability in health care requires a balance between intentionally designed systems and individual professional accountability. One element of accountability includes a process for addressing clinicians whose practices are associated with a disproportionate share of patient complaints. This study aimed to evaluate the impact of the Patient Advocacy Reporting System (PARS), a tiered intervention model to reduce patient complaints about clinicians. METHODS: A retrospective cohort study was conducted involving a southeastern U.S. orthopaedic group practice. The study assessed the implementation of the PARS program and subsequent malpractice claims from 2004 to 2020. RESULTS: The implementation of PARS was associated with an 83% reduction in malpractice claims cost per high-risk clinician after intervention (p = 0.05; Wilcoxon rank sum test). The overall practice group experienced an 87% reduction in mean annual claims cost per clinician (p = 0.007; segmented regression). The successful adoption required essential elements such as PARS champions, peer messengers, an Office of Patient Affairs, and a clear statement of practice values and professionalism expectations at the time of onboarding. CONCLUSIONS: The PARS program was successfully adopted within a surgical specialty group as a part of ongoing risk prevention and management efforts. The period following PARS was associated with a retrospectively measured reduction in malpractice claim costs. The PARS program can be effectively implemented in a large, single-specialty orthopaedic practice setting and, although not necessarily causal, was, in our case, associated with a period of reduced malpractice claim costs. CLINICAL RELEVANCE: We have learned in previous research that there are clear links between professionalism and patient outcomes (e.g., surgical complications), but agree that the focus here on medical malpractice is not directly clinical.

Assessment of the CLOT (children's likelihood of thrombosis) real-time risk prediction model of hospital-associated venous thromboembolism in children with congenital heart disease.

BACKGROUND: Children with congenital heart disease (CHD) are at high risk for hospital-associated venous thromboembolism (HA-VTE). The children's likelihood of thrombosis (CLOT) trial validated a real-time predictive model for HA-VTE using data extracted from the EHR for pediatric inpatients. We tested the hypothesis that addition of CHD specific data would improve model prediction in the CHD population. METHODS: Model performance in CHD patients from 2010 to 2022, was assessed using 3 iterations of the CLOT model: 1) the original CLOT model, 2) the original model refit using only data from the CHD cohort, and 3) the model updated with the addition of cardiopulmonary bypass time, STAT Mortality Category, height, and weight as covariates. The discrimination of the three models was quantified and compared using AUROC. RESULTS: Our CHD cohort included 1457 patient encounters (median 2.0 IQR [0.5-5.2] years-old). HA-VTE was present in 5% of our CHD cohort versus 1% in the general pediatric population. Several features from the original model were associated with thrombosis in the CHD cohort including younger age, thrombosis history, infectious disease consultation, and EHR coding of a central venous line. Lower height and weight were associated with thrombosis. HA-VTE rate was 12% (18/149) amongst those with STAT Category 4-5 operation versus 4% (49/1256) with STAT Category 1-3 operation (P < .001). Longer cardiopulmonary bypass time (124 [92-205] vs. 94 [65-136] minutes, P < .001) was associated with thrombosis. The AUROC for the original (0.80 95% CI [0.75-0.85]), refit (0.85 [0.81-0.89]), and updated (0.86 [0.81-0.90]) models demonstrated excellent discriminatory ability within the CHD cohort. CONCLUSION: The automated approach with EHR data extraction makes the applicability of such models appealing for ease of clinical use. The addition of cardiac specific features improved model discrimination; however, this benefit was marginal compared to refitting the original model to the CHD cohort. This suggests strong predictive generalized models, such as CLOT, can be optimized for cohort subsets without additional data extraction, thus reducing cost of model development and deployment.

Model-Guided Decision-Making for Thromboprophylaxis and Hospital-Acquired Thromboembolic Events Among Hospitalized Children and Adolescents: The CLOT Randomized Clinical Trial.

Importance: Rates of hospital-acquired venous thromboembolism (HA-VTE) are increasing among pediatric patients. Identifying at-risk patients for whom prophylactic interventions should be considered remains challenging. Objective: To determine whether use of a previously validated HA-VTE prognostic model, together with pediatric hematologist review, could reduce pediatric inpatient rates of HA-VTE. Design, Setting, and Participants: This pragmatic randomized clinical trial was performed from November 2, 2020, through January 31, 2022, at a single-center academic children's hospital (Monroe Carell Jr Children's Hospital at Vanderbilt). All pediatric hospital admissions (aged <22 years) under inpatient status were included and randomized. Intervention: All patients had an HA-VTE probability automatically calculated daily, which was visible to the hematology research team for patients in the intervention group. Patients with an elevated risk (predicted probability ≥2.5%) underwent additional medical record review by the research team to determine eligibility for thromboprophylaxis. Main Outcomes and Measures: The primary outcome was rate of HA-VTE. Secondary outcomes included rates of prophylactic anticoagulation and anticoagulation-associated bleeding events. Results: A total of 17 427 hospitalizations met eligibility criteria, were randomized, and were included in the primary analysis: patients had a median (IQR) age of 1.7 (0 to 11.1) years; there were 9143 (52.5%) female patients and 8284 (47.5%) male patients, and there were 445 (2.6%) Asian patients, 2739 (15.9%) Black patients, and 11 752 (67.4%) White patients. The 2 groups were evenly balanced in number (8717 in the intervention group and 8710 in the control group) and patient characteristics. A total of 58 patients (0.7%) in the control group and 77 (0.9%) in the intervention group developed HA-VTE (risk difference: 2.2 per 1000 patients; 95% CI, -0.4 to 4.8 per 1000 patients; P = .10). Recommendations to initiate thromboprophylaxis were accepted by primary clinical teams 25.8% of the time (74 of 287 hospitalizations). Minor bleeding events were rare among patients who received anticoagulation (3 of 74 [4.1%]), and no major bleeding events were observed during the study period. Among patients randomized to the control group, the model exhibited high discrimination accuracy (C statistic, 0.799, 95% CI, 0.725 to 0.856). Conclusions and Relevance: In this randomized clinical trial of the use of a HA-VTE prognostic model to reduce pediatric inpatient rates of HA-VTE, despite the use of an accurate and validated prognostic model for HA-VTE, there was substantial reluctance by primary clinical teams to initiate thromboprophylaxis as recommended. In this context, rates of HA-VTE between the control and intervention groups were not different. Future research is needed to identify improved strategies for prevention of HA-VTE and to overcome clinician concerns regarding thromboprophylaxis. Trial Registration: ClinicalTrials.gov Identifier: NCT04574895.

Unsolicited Patient Complaints Following the 21st Century Cures Act Information-Blocking Rule.

Importance: The 21st Century Cures Act includes an information-blocking rule (IBR) that requires health systems to provide patients with immediate access to their health information in the electronic medical record upon request. Patients accessing their health information before they receive an explanation from their health care team may experience confusion and may be more likely to share unsolicited patient complaints (UPCs) with their health care organization. Objective: To evaluate the quantity of UPCs about physicians before and after IBR implementation and to identify themes in UPCs that may identify patient confusion, fear, or anger related to the release of information. Design, Setting, and Participants: This retrospective cohort study was conducted with an interrupted time-series analysis of UPCs spanning January 1, 2020, to June 30, 2022. The data were obtained from a single academic medical center, Vanderbilt University Medical Center, at which the IBR was implemented on January 20, 2021. Data analysis was performed from January 11 to July 15, 2023. Exposure: Implementation of the IBR on January 20, 2021. Main Outcomes and Measures: The primary outcome was the monthly rate of UPCs before and after IBR implementation. A qualitative analysis was performed for UPCs received after IBR implementation. The Wilcoxon rank-sum test was used to compare monthly complaints between the pre- and post-IBR groups. The Pearson χ2 test was used to compare proportions of complaints by UPC category between time periods. Results: The medical center received 8495 UPCs during the study period: 3022 over 12 months before and 5473 over 18 months after institutional IBR implementation. There was no difference in the monthly proportions of UPCs per 1000 patient encounters before (median, 0.81 [IQR, 0.75-0.88]) and after (median, 0.83 [IQR, 0.77-0.89]) IBR implementation (difference in medians, -0.02 [95% CI, -0.12 to 0.07]; P =.86). Segmented regression analysis revealed no difference in monthly UPCs (ß [SE], 0.03 [0.09]; P =.72). Conclusions and Relevance: In this cohort study, implementation of the Cures Act IBR was not associated with an increase in monthly rates of UPCs. These findings suggest that review of UPCs identified as IBR-specific complaints may allow clinicians and organizations to prepare patients that their test and procedure results may be available before clinicians are able to review them and respond.

Factors and Barriers to Goals-of-Care Conversations for Patients With Cancer and Inpatient Mortality.

PURPOSE: Conversations about personal values and goals of care (GOC) at the end of life are essential in caring for patients with advanced cancer. However, GOC conversations may be influenced by patient and oncologist factors during transitions of care. METHODS: We electronically administered surveys to medical oncologists of inpatients who died from May 1, 2020, to May 31, 2021. Primary outcomes included oncologists' knowledge of inpatient death, anticipation of patient death, and recollection of GOC discussions. Secondary outcomes, including GOC documentation and advance directives (ADs), were collected retrospectively from electronic health records. Outcomes were analyzed for association with patient, oncologist, and patient-oncologist relationship factors. RESULTS: For 75 patients who died, 104/158 (66%) surveys were completed by 40 inpatient and 64 outpatient oncologists. Eighty-one oncologists (77.9%) were aware of patients' deaths, 68 (65.4%) anticipated patients' deaths within 6 months, and 67 (64.4%) recalled having GOC discussions before or during the terminal hospitalization. Outpatient oncologists were more likely to report knowledge of patient death (P < .001), as were those with longer therapeutic relationships (P < .001). Inpatient oncologists were more likely to correctly anticipate patient death (P = .014). Secondary outcomes revealed 21.3% of patients had documented GOC discussions before admission and 33.3% had ADs; patients with a longer duration of cancer diagnosis were more likely to have ADs (P = .003). Oncologist-reported barriers to GOC included unrealistic expectations from patients or family (25%) and decreased patient participation because of clinical conditions (15%). CONCLUSION: Most oncologists recalled having GOC discussions for patients with inpatient mortality, yet documentation of serious illness conversations remained suboptimal. Further studies are needed to examine barriers to GOC conversations and documentation during transitions of care and across health care settings.

Trainee Physician Milestone Ratings and Patient Complaints in Early Posttraining Practice.

Importance: Evaluation of trainees in graduate medical education training programs using Milestones has been in place since 2013. It is not known whether trainees who have lower ratings during the last year of training go on to have concerns related to interactions with patients in posttraining practice. Objective: To investigate the association between resident Milestone ratings and posttraining patient complaints. Design, Setting, and Participants: This retrospective cohort study included physicians who completed Accreditation Council for Graduate Medical Education (ACGME)-accredited programs between July 1, 2015, and June 30, 2019, and worked at a site that participated in the national Patient Advocacy Reporting System (PARS) program for at least 1 year. Milestone ratings from ACGME training programs and patient complaint data from PARS were collected. Data analysis was conducted from March 2022 to February 2023. Exposures: Lowest professionalism (P) and interpersonal and communication skills (ICS) Milestones ratings 6 months prior to the end of training. Main Outcomes and Measures: PARS year 1 index scores, based on recency and severity of complaints. Results: The cohort included 9340 physicians with median (IQR) age of 33 (31-35) years; 4516 (48.4%) were women physicians. Overall, 7001 (75.0%) had a PARS year 1 index score of 0, 2023 (21.7%) had a score of 1 to 20 (moderate), and 316 (3.4%) had a score of 21 or greater (high). Among physicians in the lowest Milestones group, 34 of 716 (4.7%) had high PARS year 1 index scores, while 105 of 3617 (2.9%) with Milestone ratings of 4.0 (proficient), had high PARS year 1 index scores. In a multivariable ordinal regression model, physicians in the 2 lowest Milestones rating groups (0-2.5 and 3.0-3.5) were statistically significantly more likely to have higher PARS year 1 index scores than the reference group with Milestones ratings of 4.0 (0-2.5 group: odds ratio, 1.2 [95% CI, 1.0-1.5]; 3.0-3.5 group: odds ratio, 1.2 [95% CI, 1.1-1.3]). Conclusions and Relevance: In this study, trainees with low Milestone ratings in P and ICS near the end of residency were at increased risk for patient complaints in their early posttraining independent physician practice. Trainees with lower Milestone ratings in P and ICS may need more support during graduate medical education training or in the early part of their posttraining practice career.

Coworker Reports about Unprofessional Behavior in Plastic Surgery.

BACKGROUND: Patient reports of unprofessional conduct by surgeons have been linked to postprocedure complications and increased risk for malpractice claims. Coworkers are also positioned to observe and report unprofessional behaviors, including concerns related to delivery of competent medical care, clear and respectful communication, integrity, and responsibility. This study compared rates of coworker concerns between plastic surgeons and other physicians (other surgical specialists and nonsurgeons), and characterized whether plastic surgery subspecialties differed in their rates of complaints. METHODS: Coworker concern data from January 1, 2014, to December 31, 2019, were retrieved from the Vanderbilt Center for Patient and Professional Advocacy's Coworker Observation Reporting System database. Specialty was classified as plastic surgery, non-plastic surgical, and nonsurgical. The plastic surgery cohort was further characterized by sex, medical school graduation year, predominant practice type (reconstructive only, aesthetic only, or hybrid), and postresidency training (ie, completion of a fellowship). RESULTS: The study cohort included 34,170 physicians (302 plastic surgeons, 7593 non-plastic surgeons, and 26,275 nonsurgeons). A greater proportion of plastic surgeons (13.6%) had one or more coworker concerns compared with nonsurgeons (10.8%) and non-plastic surgeons (6.1%) ( P < 0.001). The most prevalent concern category reported for plastic surgeons was clear and respectful communication. Among plastic surgeons who had no concerns versus those who had at least one concern, there was no significant difference when comparing sex, medical school graduation year, predominant practice type, or postresidency training. CONCLUSIONS: Plastic surgeons in this cohort study received more coworker reports than other surgeons and nonsurgical physicians. These data may be used by institutions to identify plastic surgeons at risk for preventable postprocedure complications and intervene with peer feedback intended to promote self-regulation.

Use of a real-time risk-prediction model to identify pediatric patients at risk for thromboembolic events: study protocol for the Children's Likelihood Of Thrombosis (CLOT) trial.

BACKGROUND: Pediatric patients have increasing rates of hospital-associated venous thromboembolism (HA-VTE), and while several risk-prediction models have been developed, few are designed to assess all general pediatric patients, and none has been shown to improve patient outcomes when implemented in routine clinical care. METHODS: The Children's Likelihood Of Thrombosis (CLOT) trial is an ongoing pragmatic randomized trial being conducted starting November 2, 2020, in the inpatient units at Monroe Carell Jr. Children's Hospital at Vanderbilt in Nashville, TN, USA. All admitted patients who are 21 years of age and younger are automatically enrolled in the trial and randomly assigned to receive either the current standard-of-care anticoagulation practice or the study intervention. Patients randomized to the intervention arm are assigned an HA-VTE risk probability that is calculated from a validated VTE risk-prediction model; the model is updated daily with the most recent clinical information. Patients in the intervention arm with elevated risk (predicted probability of HA-VTE ≥ 0.025) have an additional review of their clinical course by a team of dedicated hematologists, who make recommendations including pharmacologic prophylaxis with anticoagulation, if appropriate. The anticipated enrollment is approximately 15,000 patients. The primary outcome is the occurrence of HA-VTE. Secondary outcomes include initiation of anticoagulation, reasons for not initiating anticoagulation among patients for whom it was recommended, and adverse bleeding events. Subgroup analyses will be conducted among patients with elevated HA-VTE risk. DISCUSSION: This ongoing pragmatic randomized trial will provide a prospective assessment of a pediatric risk-prediction tool used to identify hospitalized patients at elevated risk of developing HA-VTE.  TRIAL REGISTRATION: ClinicalTrials.gov NCT04574895. Registered on September 28, 2020. Date of first patient enrollment: November 2, 2020.

Professionalism of Admitting and Consulting Services and Trauma Patient Outcomes.

OBJECTIVE: To determine whether trauma patients managed by an admitting or consulting service with a high proportion of physicians exhibiting patterns of unprofessional behaviors are at greater risk of complications or death. SUMMARY BACKGROUND DATA: Trauma care requires high-functioning interdisciplinary teams where professionalism, particularly modeling respect and communicating effectively, is essential. METHODS: This retrospective cohort study used data from 9 level I trauma centers that participated in a national trauma registry linked with data from a national database of unsolicited patient complaints. The cohort included trauma patients admitted January 1, 2012 through December 31, 2017. The exposure of interest was care by 1 or more high-risk services, defined as teams with a greater proportion of physicians with high numbers of patient complaints. The study outcome was death or complications within 30 days. RESULTS: Among the 71,046 patients in the cohort, 9553 (13.4%) experienced the primary outcome of complications or death, including 1875 of 16,107 patients (11.6%) with 0 high-risk services, 3788 of 28,085 patients (13.5%) with 1 high-risk service, and 3890 of 26,854 patients (14.5%) with 2+ highrisk services (P < 0.001). In logistic regression models adjusting for relevant patient, injury, and site characteristics, patients who received care from 1 or more high-risk services were at 24.1% (95% confidence interval 17.2% to 31.3%; P < 0.001) greater risk of experiencing the primary study outcome. CONCLUSIONS: Trauma patients who received care from at least 1 service with a high proportion of physicians modeling unprofessional behavior were at an increased risk of death or complications.

Palliative Care Exposure Relative to Predicted Risk of Six-Month Mortality in Hospitalized Adults.

CONTEXT: The optimal strategy for implementing mortality-predicting algorithms to facilitate clinical care, prognostic discussions, and palliative care interventions remains unknown. OBJECTIVES: To develop and validate a real-time predictive model for 180 day mortality using routinely available clinical and laboratory admission data and determine if palliative care exposure varies with predicted mortality risk. METHODS: Adult admissions between October 1, 2013 and October.1, 2017 were included for the model derivation. A separate cohort was collected between January 1, 2018 and July 31, 2020 for validation. Patients were followed for 180 days from discharge, and logistic regression with selected variables was used to estimate patients' risk for mortality. RESULTS: In the model derivation cohort, 7963 events of 180 day mortality (4.5% event rate) were observed. Median age was 53.0 (IQR 24.0-66.0) with 92,734 females (52.5%). Variables with strongest association with 180 day mortality included: Braden Score (OR 0.83; 95% CI 0.82-0.84); admission Do Not Resuscitate orders (OR 2.61; 95% CI 2.43-2.79); admission service and admission status. The model yielded excellent discriminatory ability in both the derivation (c-statistic 0.873; 95% CI 0.870-0.877; Brier score 0.04) and validation cohorts (c-statistic 0.844; 95% CI 0.840-0.847; Brier score 0.072). Inpatient palliative care consultations increased from 3% of minimal-risk encounters to 41% of high-risk encounters (P < 0.01). CONCLUSION: We developed and temporally validated a predictive mortality model for adults from a large retrospective cohort, which helps quantify the potential need for palliative care referrals based on risk strata. Machine learning algorithms for mortality require clinical interpretation, and additional studies are needed to design patient-centered and risk-specific interventions.

A Predictive Model of Reintubation After Cardiac Surgery Using the Electronic Health Record.

BACKGROUND: Reintubation and prolonged intubation after cardiac surgery are associated with significant complications. Despite these competing risks, providers frequently extubate patients with limited insight into the risk of reintubation at the time of extubation. Achieving timely, successful extubation remains a significant clinical challenge. METHODS: Based on an analysis of 2835 patients undergoing cardiac surgery at our institution between November 2017 and July 2020, we developed a model for an individual's risk of reintubation at the time of extubation. Predictors were screened for inclusion in the model based on clinical plausibility and availability at the time of extubation. Rigorous data reduction methods were used to create a model that could be easily integrated into clinical workflow at the time of extubation. RESULTS: In total, 90 patients (3.2%) were reintubated within 48 hours of initial extubation. Number of inotropes (1 [adjusted odds ratio (OR), 15.4; 95% confidence interval (CI) 6.5-47.6; P < .001], ≥2 [OR, 62.7; 95% CI 14.3-279.5; P < .001]); dexmedetomidine dose (OR, 3.0 [per µg/kg/h]; 95% CI 1.9-4.7; P < .001), time to extubation (OR, 1.04 [per 6-hour increase]; 95% CI 1.02-1.05; P < .001), and respiratory rate (OR, 1.04 [per breath/min]; 95% CI 1.01-1.07; P < .001) were the best predictors for the model, which displayed excellent discriminative capacity (area under the receiver operating characteristic, 0.86; 95% CI 0.84-0.89). CONCLUSIONS: An improved understanding of reintubation risk may lead to improved decision-making at extubation and targeted interventions to decrease reintubation in high-risk patients. Future studies are needed to optimize timing of extubation.

Effects of financial incentives on volunteering for clinical trials: A randomized vignette experiment.

BACKGROUND: Financial incentives may aid recruitment to clinical trials, but evidence regarding risk/burden-driven variability in participant preferences for incentives is limited. We developed and tested a framework to support real-world decisions on recruitment budget. METHODS: We included two phases: an Anchoring Survey, to ensure we could capture perceived unpleasantness on a range of life events, and a Vignette Experiment, to explore relationships between financial incentives and participants' perceived risk/burden and willingness to participate in high- and low-risk/burden versions of five vignettes drawn from common research activities. We compared vignette ratings to identify similarly rated life events from the Anchoring Survey to contextualize ratings of study risk. RESULTS: In our Anchoring Survey (n = 643), mean ratings (scale 1 = lowest risk/burden to 5 = highest risk/burden) indicated that the questions made sense to participants, with highest risk assigned to losing house in a fire (4.72), and lowest risk assigned to having blood pressure taken (1.13). In the Vignette Experiment (n = 534), logistic regression indicated that amount of offered financial incentive and perceived risk/burden level were the top two drivers of willingness to participate in four of the five vignettes. Comparison of event ratings in the Anchoring Survey with the Vignette Experiment ratings suggested reasonable concordance on severity of risk/burden. CONCLUSIONS: We demonstrated feasibility of a framework for assessing participant perceptions of risk for study activities and discerned directionality of relationship between financial incentives and willingness to participate. Future work will explore use of this framework as an evidence-gathering approach for gauging appropriate incentives in real-world study contexts.

The first 1000 symptomatic pediatric SARS-CoV-2 infections in an integrated health care system: a prospective cohort study.

BACKGROUND: The spectrum of illness and predictors of severity among children with SARS-CoV-2 infection are incompletely understood. METHODS: Active surveillance was performed for SARS-CoV-2 by polymerase chain reaction among symptomatic pediatric patients in a quaternary care academic hospital laboratory beginning March 12, 2020. We obtained sociodemographic and clinical data 5 (+/-3) and 30 days after diagnosis via phone follow-up and medical record review. Logistic regression was used to assess predictors of hospitalization. RESULTS: The first 1000 symptomatic pediatric patients were diagnosed in our institution between March 13, 2020 and September 28, 2020. Cough (52 %), headache (43 %), and sore throat (36 %) were the most common symptoms. Forty-one (4 %) were hospitalized; 8 required ICU admission, and 2 required mechanical ventilation (< 1 %). One patient developed multisystem inflammatory syndrome in children; one death was possibly associated with SARS-CoV-2 infection. Symptom resolution occurred by follow-up day 5 in 398/892 (45 %) patients and by day 30 in 443/471 (94 %) patients. Pre-existing medical condition (OR 7.7; 95 % CI 3.9-16.0), dyspnea (OR 6.8; 95 % CI 3.2-14.1), Black race or Hispanic ethnicity (OR 2.7; 95 % CI 1.3-5.5), and vomiting (OR 5.4; 95 % CI 1.2-20.6) were the strongest predictors of hospitalization. The model displayed excellent discriminative ability (AUC = 0.82, 95 % CI 0.76-0.88, Brier score = 0.03). CONCLUSIONS: In 1000 pediatric patients with systematic follow-up, most SARS-CoV-2 infections were mild, brief, and rarely required hospitalization. Pediatric predictors of hospitalization included comorbid conditions, Black race, Hispanic ethnicity, dyspnea and vomiting and were distinct from those reported among adults.

A Real-time Risk-Prediction Model for Pediatric Venous Thromboembolic Events.

BACKGROUND: Hospital-associated venous thromboembolism (HA-VTE) is an increasing cause of morbidity in pediatric populations, yet identification of high-risk patients remains challenging. General pediatric models have been derived from case-control studies, but few have been validated. We developed and validated a predictive model for pediatric HA-VTE using a large, retrospective cohort. METHODS: The derivation cohort included 111 352 admissions to Monroe Carell Jr. Children's Hospital at Vanderbilt. Potential variables were identified a priori, and corresponding data were extracted. Logistic regression was used to estimate the association of potential risk factors with development of HA-VTE. Variable inclusion in the model was based on univariate analysis, availability in routine medical records, and clinician expertise. The model was validated by using a separate cohort with 44 138 admissions. RESULTS: A total of 815 encounters were identified with HA-VTE in the derivation cohort. Variables strongly associated with HA-VTE include history of thrombosis (odds ratio [OR] 8.7; 95% confidence interval [CI] 6.6-11.3; P < .01), presence of a central line (OR 4.9; 95% CI 4.0-5.8; P < .01), and patients with cardiology conditions (OR 4.0; 95% CI 3.3-4.8; P < .01). Eleven variables were included, which yielded excellent discriminatory ability in both the derivation cohort (concordance statistic = 0.908) and the validation cohort (concordance statistic = 0.904). CONCLUSIONS: We created and validated a risk-prediction model that identifies pediatric patients at risk for HA-VTE development. We anticipate early identification of high-risk patients will increase prophylactic interventions and decrease the incidence of pediatric HA-VTE.

Unsolicited patient complaints among radiation, medical, and surgical oncologists.

BACKGROUND: Unsolicited patient complaints (UPCs) about physician practices are nonrandomly associated with malpractice claims and clinical quality. The authors evaluated the distributions and types of UPCs associated with oncologists by specialty and assessed oncologist characteristics associated with UPCs. METHODS: This retrospective study reviewed UPCs associated with US radiation oncologists (ROs), medical oncologists (MOs), and surgical oncologists (SOs) from 35 health care systems from 2015 to 2018. Average total UPCs were compared by specialty in addition to sex, medical school graduation year, degree, medical school location, residency location, practice setting, and practice region. For continuous variables, linear regression was used to test for an association with total complaints. RESULTS: The study included 1576 physicians: 318 ROs, 1020 MOs, and 238 SOs. The average number of UPCs per physician was different and depended on the oncologic specialty: ROs had significantly fewer complaints (1.28; 95% confidence interval [CI], 1.02-1.54) than MOs (3.81; 95% CI, 3.52-4.10) and SOs (6.89; 95% CI, 5.99-7.79; P < .0001). In a multivariable analysis, oncologic specialty, recency of graduation, and academic practice were predictive of higher total UPCs (P < .05). UPCs described concerns with care and treatment (42.8%), communication (26.4%), accessibility (17.5%), concern for patient (10.3%), and billing (2.9%). CONCLUSIONS: ROs had significantly fewer complaints than MOs and SOs and may have a lower risk of malpractice claims as a group. In addition to oncologic specialty, a more recent year of medical school graduation and working at an academic center were independent risk factors for UPCs. Further research is needed to clarify the reasons underlying these associations and to identify interventions that decrease UPCs and associated risks. LAY SUMMARY: This study of 1576 oncologists found that radiation oncologists had significantly fewer complaints than medical oncologists, who in turn had significantly fewer complaints than surgical oncologists. Other characteristics associated with more patient complaints included recency of medical school graduation and practice in an academic setting. Oncologists' patient complaints provide information that may have practical applications for patient safety and risk management. Understanding and addressing the characteristics that increase the risk for complaints could improve patients' experiences and outcomes.

Early Predictors of Performance Deficiencies in Academic Faculty: Pre-Employment Red Flags.

OBJECTIVE: To determine whether faculty who had red flags (unprofessional behavior, delayed response to queries, or delayed submission of required documentation) during pre-employment were more likely to have performance deficiencies than faculty who did not have red flags. METHODS: The study included 187 faculty consecutively hired in a Department of Pediatrics in a large academic health system from 2013 to 2018. Faculty with and without pre-employment red flags were compared to identify the proportion who had subsequent performance deficiencies related to documentation, unprofessional behavior, performance, or premature departure from the faculty. RESULTS: Most of the hired faculty were female (127, 0.68), physicians (136, 0.73), and clinicians or clinician-educators (124, 0.67). Sixteen faculty (0.09) had pre-employment red flags. In the 3 years after hiring, 31 (0.17) of the faculty cohort had at least 1 performance deficiency. Faculty with pre-employment red flags were more than 4 times as likely to experience a performance deficiency during follow-up (0.56 vs 0.13, P < .001). The hazard ratio for performance deficiency comparing faculty with pre-employment red flags to those without was 5.98 (95% confidence interval 2.73-13.1, P < .0001). CONCLUSIONS: Faculty who had pre-employment red flags were significantly more likely to experience subsequent performance deficiencies. Given the substantial investment that individuals and academic medical centers make in recruiting and hiring new faculty, efforts to identify and assist faculty members at risk provide academic departments opportunities to provide the best environment for success for all faculty.

Characteristics of Inpatient Units Associated With Sustained Hand Hygiene Compliance.

OBJECTIVES: Following institution of a hand hygiene (HH) program at an academic medical center, HH compliance increased from 58% to 92% for 3 years. Some inpatient units modeled early, sustained increases, and others exhibited protracted improvement rates. We examined the association between patterns of HH compliance improvement and unit characteristics. METHODS: Adult inpatient units (N = 35) were categorized into the following three tiers based on their pattern of HH compliance: early adopters, nonsustained and late adopters, and laggards. Unit-based culture measures were collected, including nursing practice environment scores (National Database of Nursing Quality Indicators [NDNQI]), patient rated quality and teamwork (Hospital Consumer Assessment of Healthcare Provider and Systems), patient complaint rates, case mix index, staff turnover rates, and patient volume. Associations between variables and the binary outcome of laggard (n = 18) versus nonlaggard (n = 17) were tested using a Mann-Whitney U test. Multivariate analysis was performed using an ordinal regression model. RESULTS: In direct comparison, laggard units had clinically relevant differences in NDNQI scores, Hospital Consumer Assessment of Healthcare Provider and Systems scores, case mix index, patient complaints, patient volume, and staff turnover. The results were not statistically significant. In the multivariate model, the predictor variables explained a significant proportion of the variability associated with laggard status, (R2 = 0.35, P = 0.0481) and identified NDNQI scores and patient complaints as statistically significant. CONCLUSIONS: Uptake of an HH program was associated with factors related to a unit's safety culture. In particular, NDNQI scores and patient complaint rates might be used to assist in identifying units that may require additional attention during implementation of an HH quality improvement program.

Impact of a Follow-up Telephone Call Program on 30-Day Readmissions (FUTR-30): A Pragmatic Randomized Controlled Real-world Effectiveness Trial.

BACKGROUND: Telephone call programs are a common intervention used to improve patients' transition to outpatient care after hospital discharge. OBJECTIVE: To examine the impact of a follow-up telephone call program as a readmission reduction initiative. RESEARCH DESIGN: Pragmatic randomized controlled real-world effectiveness trial. SUBJECTS: We enrolled and randomized all patients discharged home from a hospital general medicine service to a follow-up telephone call program or usual care discharge. Patients discharged against medical advice were excluded. The intervention was a hospital program, delivering a semistructured follow-up telephone call from a nurse within 3-7 days of discharge, designed to assess understanding and provide education, and assistance to support discharge plan implementation. MEASURES: Our primary endpoint was hospital inpatient readmission within 30 days identified by the electronic health record. Secondary endpoints included observation readmission, emergency department revisit, and mortality within 30 days, and patient experience ratings. RESULTS: All 3054 patients discharged home were enrolled and randomized to the telephone call program (n=1534) or usual care discharge (n=1520). Using a prespecified intention-to-treat analysis, we found no evidence supporting differences in 30-day inpatient readmissions [14.9% vs. 15.3%; difference -0.4 (95% confidence interval, 95% CI), -2.9 to 2.1; P=0.76], observation readmissions [3.8% vs. 3.6%; difference 0.2 (95% CI, -1.1 to 1.6); P=0.74], emergency department revisits [6.1% vs. 5.4%; difference 0.7 (95% CI, -1.0 to 2.3); P=0.43], or mortality [4.4% vs. 4.9%; difference -0.5 (95% CI, -2.0 to 1.0); P=0.51] between telephone call and usual care groups. CONCLUSIONS: We found no evidence of an impact on 30-day readmissions or mortality due to the postdischarge telephone call program.