Incidence of Respiratory Syncytial Virus Infection in Older Adults Before and During the COVID-19 Pandemic.

Importance: Little is known about the burden and outcomes of respiratory syncytial virus (RSV)-positive acute respiratory infection (ARI) in community-dwelling older adults. Objective: To assess the incidence of RSV-positive ARI before and during the COVID-19 pandemic, and to assess outcomes for RSV-positive ARI in older adults. Design, Setting, and Participants: This was a community-based cohort study of adults residing in southeast Minnesota that followed up with 2325 adults aged 50 years or older for 2 RSV seasons (2019-2021) to assess the incidence of RSV-positive ARI. The study assessed outcomes at 2 to 4 weeks, 6 to 7 months, and 12 to 13 months after RSV-positive ARI. Exposure: RSV-positive and -negative ARI. Main Outcomes and Measures: RSV status was the main study outcome. Incidence and attack rates of RSV-positive ARI were calculated during each RSV season, including before (October 2019 to April 2020) and during (October 2020 to April 2021) COVID-19 pandemic, and further calculated during non-RSV season (May to September 2021) for assessing impact of COVID-19. The self-reported quality of life (QOL) by Short-Form Health Survey-36 (SF-36) and physical functional measures (eg, 6-minute walk and spirometry) at each time point was assessed. Results: In this study of 2325 participants, the median (range) age of study participants was 67 (50-98) years, 1380 (59%) were female, and 2240 (96%) were non-Hispanic White individuals. The prepandemic incidence rate of RSV-positive ARI was 48.6 (95% CI, 36.9-62.9) per 1000 person-years with a 2.50% (95% CI, 1.90%-3.21%) attack rate. No RSV-positive ARI case was identified during the COVID-19 pandemic RSV season. Incidence of 10.2 (95% CI, 4.1-21.1) per 1000 person-years and attack rate of 0.42%; (95% CI, 0.17%-0.86%) were observed during the summer of 2021. Based on prepandemic RSV season results, participants with RSV-positive ARI (vs matched RSV-negative ARI) reported significantly lower QOL adjusted mean difference (limitations due to physical health, -16.7 [95% CI, -31.8 to -1.8]; fatigue, -8.4 [95% CI, -14.3 to -2.4]; and difficulty in social functioning, -11.9 [95% CI, -19.8 to -4.0] within 2 to 4 weeks after RSV-positive ARI [ie, short-term outcome]). Compared with participants with RSV-negative ARI, those with RSV-positive ARI also had lower QOL (fatigue: -4.0 [95% CI, -8.5 to -1.3]; difficulty in social functioning, -5.8 [95% CI, -10.3 to -1.3]; and limitation due to emotional problem, -7.0 [95% CI, -12.7 to -1.3] at 6 to 7 months after RSV-positive ARI [intermediate-term outcome]; fatigue, -4.4 [95% CI, -7.3 to -1.5]; difficulty in social functioning, -5.2 [95% CI, -8.7 to -1.7] and limitation due to emotional problem, -5.7 [95% CI, -10.7 to -0.6] at 12-13 months after RSV-positive ARI [ie, long-term outcomes]) independent of age, sex, race and/or ethnicity, socioeconomic status, and high-risk comorbidities. Conclusions and Relevance: In this cohort study, the burden of RSV-positive ARI in older adults during the pre-COVID-19 period was substantial. After a reduction of RSV-positive ARI incidence from October 2020 to April 2021, RSV-positive ARI re-emerged during the summer of 2021. RSV-positive ARI was associated with significant long-term lower QOL beyond the short-term lower QOL in older adults.

Influenza infection is not associated with phenotypical frailty in older patients, a prospective cohort study.

Background and Aims: Influenza is a challenging infectious illness for older adults. It is not completely clear whether influenza is associated with frailty or functional decline. We sought to determine the association between incident influenza infection and frailty and prefrailty in community patients over 50 years of age. We also investigated the association between influenza vaccination and frailty and prefrailty as a secondary aim. Methods: This was a prospective community cohort study from October 2019 to November 2020 in participants over 50 years. The primary outcome was the development of frailty as defined by three of five frailty criteria (slow gait speed, low grip strength, 5% weight loss, low energy, and low physical functioning). The primary predictor was a positive polymerase chain reaction (PCR) for influenza infection. Influenza vaccination was based on electronic health record reviewing 1 year before enrollment. We reported the relationship between influenza and frailty by calculating odds ratios (OR) with 95% confidence intervals (CI) after adjustment for age, sex, socioeconomic status, Charlson Comorbidity Index (CCI), influenza vaccine, and previous self-rated frailty from multinomial logistic regression model comparing frail and prefrail to nonfrail subjects. Results: In 1135 participants, the median age was 67 years (interquartile range  60-74), with 41% men. Eighty-one participants had PCR-confirmed influenza (7.1%). Frailty was not associated with influenza, with an OR of 0.50 (95% CI 0.17-1.43) for frail participants compared to nonfrail participants. Influenza vaccination is associated with frailty, with an OR of 1.69 (95% CI 1.09-2.63) for frail compared to nonfrail. Frailty was associated with a higher CCI with an OR of 1.52 (95% CI 1.31-1.76). Conclusion: We did not find a relationship between influenza infection and frailty. We found higher vaccination rates in participants with frailty compared to nonfrail participants While influenza was not associated with frailty, future work may involve longer follow-up.

Outcomes of a Nursing Home-to-Community Care Transition Program.

OBJECTIVES: Most transitional care initiatives to reduce rehospitalization have focused on the transition that occurs between a patient's hospital discharge and return home. However, many patients are discharged from a skilled nursing facility (SNF) to their homes. The goal was to evaluate the effectiveness of the Mayo Clinic Care Transitions (MCCT) program (hereafter called program) among patients discharged from SNFs to their homes. DESIGN: Propensity-matched control-intervention trial. INTERVENTION: Patients in the intervention group received care management following nursing stay (a home visit and nursing phone calls). SETTING AND PARTICIPANTS: Patients enrolled after discharge from an SNF to home were matched to patients who did not receive intervention because of refusal, program capacity, or distance. Patients were aged ≥60 years, at high risk for hospitalization, and discharged from an SNF. METHODS: Program enrollees were matched through propensity score to nonenrollees on the basis of age, sex, comorbid health burden, and mortality risk score. Conditional logistic regression analysis examined 30-day hospitalization and emergency department (ED) use; Cox proportional hazards analyses examined 180-day hospital stay and ED use. RESULTS: Each group comprised 160 patients [mean (standard deviation) age, 85.4 (7.4) years]. Thirty-day hospitalization and ED rates were 4.4% and 10.0% in the program group and 3.8% and 10.0% in the group with usual care (P = .76 for hospitalization; P > .99 for ED). At 180 days, hospitalization and ED rates were 30.6% and 46.3% for program patients compared with 11.3% and 25.0% in the comparison group (P < .001). CONCLUSIONS AND IMPLICATIONS: We found no evidence of reduced hospitalization or ED visits by program patients vs the comparison group. Such findings are crucial because they illustrate how aggressive stabilization care within the SNF may mitigate the program role. Furthermore, we found higher ED and hospitalization rates at 180 days in program patients than the comparison group.

Using machine learning to improve the accuracy of patient deterioration predictions: Mayo Clinic Early Warning Score (MC-EWS).

OBJECTIVE: We aimed to develop a model for accurate prediction of general care inpatient deterioration. MATERIALS AND METHODS: Training and internal validation datasets were built using 2-year data from a quaternary hospital in the Midwest. Model training used gradient boosting and feature engineering (clinically relevant interactions, time-series information) to predict general care inpatient deterioration (resuscitation call, intensive care unit transfer, or rapid response team call) in 24 hours. Data from a tertiary care hospital in the Southwest were used for external validation. C-statistic, sensitivity, positive predictive value, and alert rate were calculated for different cutoffs and compared with the National Early Warning Score. Sensitivity analysis evaluated prediction of intensive care unit transfer or resuscitation call. RESULTS: Training, internal validation, and external validation datasets included 24 500, 25 784 and 53 956 hospitalizations, respectively. The Mayo Clinic Early Warning Score (MC-EWS) demonstrated excellent discrimination in both the internal and external validation datasets (C-statistic = 0.913, 0.937, respectively), and results were consistent in the sensitivity analysis (C-statistic = 0.932 in external validation). At a sensitivity of 73%, MC-EWS would generate 0.7 alerts per day per 10 patients, 45% less than the National Early Warning Score. DISCUSSION: Low alert rates are important for implementation of an alert system. Other early warning scores developed for the general care ward have achieved lower discrimination overall compared with MC-EWS, likely because MC-EWS includes both nursing assessments and extensive feature engineering. CONCLUSIONS: MC-EWS achieved superior prediction of general care inpatient deterioration using sophisticated feature engineering and a machine learning approach, reducing alert rate.

Impact of a Program to Improve Venous Thromboembolism Prophylaxis on Incidence of Thromboembolism and Bleeding Rates in Hospitalized Patients During Implementation of Programs to Improve Venous Thromboembolism Prophylaxis.

OBJECTIVE: To study the impact of multiphase quality improvement efforts to enhance appropriate use of chemical and mechanical venous thromboembolism (VTE) prophylaxis (VTEP) on the rate of hospital-acquired VTE and determine whether efforts have been associated with increased bleeding complications. PATIENTS AND METHODS: All adult inpatients discharged between January 1, 2005, and December 31, 2015, were included in the study. Retrospective interrupted time series analysis compared VTEP performance, VTE outcomes, and unintended consequences (derived from linked administrative and clinical data) across 5 improvement phases: baseline (January 1, 2005-December 31, 2006), paper order set phase (January 1, 2007-February 9, 2009), electronic order set phase (February 10, 2009-December 16, 2009), active reminder phase (December 17, 2009-May 31, 2012), and maintenance phase (June 1, 2012-September 30, 2015). RESULTS: Guideline VTEP plan adherence at the end of the study period (including documenting contraindications) reached 88.8% (654,138 of 736,384 patient days). Delivery of pharmacological VTEP increased from 43.9% (49,155 of 111,906 patients) to 60.8% (75,784 of 124,676 patients); delivery of mechanical or pharmacological VTEP increased less (65.0% [431,791 of 664,087 patient days] to 67.4% [496,625 of 736,384 patient days]). Mean VTE rates decreased from 4.6 per 1000 hospitalizations (21.7 VTEs per month) at baseline to 4.3 per 1000 hospitalizations (18.0 VTEs per month) during the maintenance phase (P<.001). More than 97% of patients who had development of VTE (534 of 548) received VTEP, but 65.7% (360 of 548) experienced gaps of 1 or more days in VTEP delivery. Measured in-hospital bleeding rates were fairly consistent over the study (4.6% [5,198 of 111,906 patients] at baseline to 5.3% [6,662 of 124,676 patients] during the reminder phase). There was little change in rates of 7-day readmission with bleeding or VTE. CONCLUSION: Our VTEP project improved guideline compliance, increased the proportion of patients receiving VTEP, and was associated with a decrease in VTE. Gaps in VTEP delivery occurred despite protocoled order sets and electronic feedback. Further improvements in VTE may require new approaches.

The fifth vital sign? Nurse worry predicts inpatient deterioration within 24 hours.

INTRODUCTION: Identification of hospitalized patients with suddenly unfavorable clinical course remains challenging. Models using objective data elements from the electronic health record may miss important sources of information available to nurses. METHODS: We recorded nurses' perception of patient potential for deterioration in 2 medical and 2 surgical adult hospital units using a 5-point score at the start of the shift (the Worry Factor [WF]), and any time a change or an increase was noted by the nurse. Cases were evaluated by three reviewers. Intensive care unit (ICU) transfers were also tracked. RESULTS: 31 159 patient-shifts were recorded for 3185 unique patients during 3551 hospitalizations, with 169 total outcome events. Out of 492 potential deterioration events identified, 380 (77%) were confirmed by reviewers as true deterioration events. Likelihood ratios for ICU transfer were 17.8 (15.2-20.9) in the 24 hours following a WF > 2, and 40.4 (27.1-60.1) following a WF > 3. Accuracy rates were significantly higher in nurses with over a year of experience (68% vs 79%, P = 0.04). The area under the receiver operator characteristic curve (AUROC) was 0.92 for the prediction of ICU transfer within 24 hours. DISCUSSION: This is a higher accuracy than most published early warning scores. CONCLUSION: Nurses' pattern recognition and sense of worry can provide important information for the detection of acute physiological deterioration and should be included in the electronic medical record.

Evaluating Automated Rules for Rapid Response System Alarm Triggers in Medical and Surgical Patients.

BACKGROUND: The use of rapid response systems (RRS), which were designed to bring clinicians with critical care expertise to the bedside to prevent unnecessary deaths, has increased. RRS rely on accurate detection of acute deterioration events. Early warning scores (EWS) have been used for this purpose but were developed using heterogeneous populations. Predictive performance may differ in medical vs surgical patients. OBJECTIVE: To evaluate the performance of published EWS in medical vs surgical patient populations. DESIGN: Retrospective cohort study. SETTING: Two tertiary care academic medical center hospitals in the Midwest totaling more than 1500 beds. PATIENTS: All patients discharged from January to December 2011. INTERVENTION: None. MEASUREMENTS: Time-stamped longitudinal database of patient variables and outcomes, categorized as surgical or medical. Outcomes included unscheduled transfers to the intensive care unit, activation of the RRS, and calls for cardiorespiratory resuscitation ("resuscitation call"). The EWS were calculated and updated with every new patient variable entry over time. Scores were considered accurate if they predicted an outcome in the following 24 hours. RESULTS: All EWS demonstrated higher performance within the medical population as compared to surgical: higher positive predictive value (P < .0001 for all scores) and sensitivity (P < .0001 for all scores). All EWS had positive predictive values below 25%. CONCLUSIONS: The overall poor performance of the evaluated EWS was marginally better in medical patients when compared to surgical patients. Journal of Hospital Medicine 2017;12:217-223.

Individualizing and optimizing the use of early warning scores in acute medical care for deteriorating hospitalized patients.

AIM: While early warning scores (EWS) have the potential to identify physiological deterioration in an acute care setting, the implementation of EWS in clinical practice has yet to be fully realized. The primary aim of this study is to identify optimal patient-centered rapid response team (RRT) activation rules using electronic medical records (EMR)-derived Markovian models. METHODS: The setting for the observational cohort study included 38,356 adult general floor patients hospitalized in 2011. The national early warning score (NEWS) was used to measure the patient health condition. Chi-square and Kruskal Wallis tests were used to identify statistically significant subpopulations as a function of the admission type (medical or surgical), frailty as measured by the Braden skin score, and history of prior clinical deterioration (RRT, cardiopulmonary arrest, or unscheduled ICU transfer). RESULTS: Statistical tests identified 12 statistically significant subpopulations which differed clinically, as measured by length of stay and time to re-admission (P < .001). The Chi-square test of independence results showed a dependency structure between subsequent states in the embedded Markov chains (P < .001). The SMDP models identified two sets of subpopulation-specific RRT activation rules for each statistically unique subpopulation. Clinical deterioration experience in prior hospitalizations did not change the RRT activation rules. The thresholds differed as a function of admission type and frailty. CONCLUSIONS: EWS were used to identify personalized thresholds for RRT activation for statistically significant Markovian patient subpopulations as a function of frailty and admission type. The full potential of EWS for personalizing acute care delivery is yet to be realized.

Widely used track and trigger scores: are they ready for automation in practice?

INTRODUCTION: Early Warning Scores (EWS) are widely used for early recognition of patient deterioration. Automated alarm/alerts have been recommended as a desirable characteristic for detection systems of patient deterioration. We undertook a comparative analysis of performance characteristics of common EWS methods to assess how they would function if automated. METHODS: We evaluated the most widely used EWS systems (MEWS, SEWS, GMEWS, Worthing, ViEWS and NEWS) and the Rapid Response Team (RRT) activation criteria in use in our institution. We compared their ability to predict the composite outcome of Resuscitation call, RRS activation or unplanned transfer to the ICU, in a time-dependent manner (3, 8, 12, 24 and 36 h after the observation) by determining the sensitivity, specificity and positive predictive values (PPV). We used a large vital signs database (6,948,689 unique time points) from 34,898 unique consecutive hospitalized patients. RESULTS: PPVs ranged from less than 0.01 (Worthing, 3 h) to 0.21 (GMEWS, 36 h). Sensitivity ranged from 0.07 (GMEWS, 3 h) to 0.75 (ViEWS, 36 h). Used in an automated fashion, these would correspond to 1040-215,020 false positive alerts per year. CONCLUSIONS: When the evaluation is performed in a time-sensitive manner, the most widely used weighted track-and-trigger scores do not offer good predictive capabilities for use as criteria for an automated alarm system. For the implementation of an automated alarm system, better criteria need to be developed and validated before implementation.