Low cardiac output syndrome complicates recovery after cardiac surgery. We examined the incidence and risk factors for low cardiac output syndrome and its association with postoperative mortality, ...morbidity, resource use, and cost.
This cross-sectional retrospective observational study examined patients having cardiac surgery captured in the Premier Healthcare Database. Low cardiac output syndrome was defined as the requirement for postoperative mechanical circulatory support and/or hemodynamic instability requiring prolonged inotropic support. Incidence, risk factors, and association of low cardiac output syndrome with postoperative outcomes, including mortality, hospital and intensive care unit length of stay, hospital readmission, and cost at 30 days, 90 days, and 6 months, were examined.
Among 59,810 patients from 164 hospitals having cardiac surgery between July 1, 2012, and June 30, 2014, low cardiac output syndrome developed in 6067 (10.1%) patients. Patients presenting in cardiogenic shock or systolic (± diastolic) heart failure were at greatest risk. Risk-adjusted in-hospital mortality was 12-fold greater with low cardiac output syndrome (odds ratio, 12.0; 95% confidence interval, 10.6-13.5). Risk-adjusted hospital costs (2019$; median Q1, Q3) were $64,041 21,439 in patients who developed low cardiac output syndrome versus $48,086 16,098 without; P < .001. Increased costs were driven by longer risk-adjusted hospital stay (10.1 4.5 vs 8.5 3.8 days); P < .001, intensive care unit (5.5 2.5 vs 3.3 1.5 days; P < .001) stay, and all-cause 30-day adjusted hospital readmission rates (mean SD 16.6 8.2% vs 13.9 7.2%; P < .001).
Cardiac surgical patients who develop postoperative low cardiac output syndrome suffer greater mortality and have greater resource use, health care costs, and all-cause readmission, which informs perioperative decision making, and impacts hospital performance metrics and federal priority to reduce health care costs.
Graphical abstract that describes the study population, provides the definition of low cardiac output syndrome, and includes the percentage of patients who experience postoperative low cardiac output syndrome overall and by procedure. The adjusted hospital costs, ICU stay, and readmission data are compared in patients with and without low cardiac output syndrome. Most important risk factors for low cardiac output syndrome and adjusted risk for mortality are also shown. Display omitted
Horn SD, DeJong G, Smout RJ, Gassaway J, James R, Conroy B. Stroke rehabilitation patients, practice, and outcomes: is earlier and more aggressive therapy better?
To examine associations of patient ...characteristics, rehabilitation therapies, neurotropic medications, nutritional support, and timing of initiation of rehabilitation with functional outcomes and discharge destination for inpatient stroke rehabilitation patients.
Prospective observational cohort study.
Five U.S. inpatient rehabilitation facilities.
Post-stroke rehabilitation patients (N=830; age, >18 y) with moderate or severe strokes, from the Post-Stroke Rehabilitation Outcomes Project database.
Not applicable.
Discharge total, motor, and cognitive FIM scores and discharge destination.
Controlling for patient differences, various activities and interventions were associated with better outcomes including earlier initiation of rehabilitation, more time spent per day in higher-level rehabilitation activities such as gait, upper-extremity control, and problem solving, use of newer psychiatric medications, and enteral feeding. Several findings part with conventional practice, such as starting gait training in the first 3 hours of physical therapy, even for low-level patients, was associated with better outcomes.
Specific therapy activities and interventions are associated with better outcomes. Earlier rehabilitation admission, higher-level activities early in the rehabilitation process, tube feeding, and newer medications are associated with better stroke rehabilitation outcomes.
Background
Evidence-based digital health programs have shown efficacy in being primary tools to improve emotional and mental health, as well as offering supplementary support to individuals ...undergoing psychotherapy for anxiety, depression, and other mental health disorders. However, information is lacking about the dose response to digital mental health interventions.
Objective
The objective of the study was to examine the effect of time in program and program usage on symptom change among individuals enrolled in a real-world comprehensive digital mental health program (myStrength) who are experiencing severe anxiety or depression.
Methods
Eligible participants (N=18,626) were adults aged 18 years and older who were enrolled in myStrength for at least four weeks as part of their employee wellness benefit program, who completed baseline, the 2-week, 2-month, and 6-month surveys querying symptoms of anxiety (Generalized Anxiety Disorder–7 GAD-7) and depression (Patient Health Questionnaire–9 PHQ-9). Linear growth curve models were used to analyze the effect of average weekly program usage on subsequent GAD-7 and PHQ-9 scores for participants with scores indicating severe anxiety (GAD-7≥15) or depression (PHQ-9≥15). All models were adjusted for baseline score and demographics.
Results
Participants in the study (N=1519) were 77.4% female (1176/1519), had a mean age of 45 years (SD 14 years), and had an average enrollment time of 3 months. At baseline, participants reported an average of 9.39 (SD 6.04) on the GAD-7 and 11.0 (SD 6.6) on the PHQ-9. Those who reported 6-month results had an average of 8.18 (SD 6.15) on the GAD-7 and 9.18 (SD 6.79) on the PHQ-9. Participants with severe scores (n=506) experienced a significant improvement of 2.97 (SE 0.35) and 3.97 (SE 0.46) at each time point for anxiety and depression, respectively (t=–8.53 and t=–8.69, respectively; Ps<.001). Those with severe baseline scores also saw a reduction of 0.27 (SE 0.08) and 0.25 (SE 0.09) points in anxiety and depression, respectively, for each additional program activity per week (t=–3.47 and t=–2.66, respectively; Ps<.05).
Conclusions
For participants with severe baseline scores, the study found a clinically significant reduction of approximately 9 points for anxiety and 12 points for depression after 6 months of enrollment, suggesting that interventions targeting mental health must maintain active, ongoing engagement when symptoms are present and be available as a continuous resource to maximize clinical impact, specifically in those experiencing severe anxiety or depression. Moreover, a dosing effect was shown, indicating improvement in outcomes among participants who engaged with the program every other day for both anxiety and depression. This suggests that digital mental health programs that provide both interesting and evidence-based activities could be more successful in further improving mental health outcomes.
IntroductionTo investigate the impact of the digital Livongo Diabetes Prevention Program (DPP) on weight at 12 months, understand participants’ self-monitoring behaviors associated with greater ...weight loss, and evaluate the impact of coaching interactions on more frequent self-monitoring behaviors.Research design and methodsA retrospective analysis was performed using data from 2037 participants enrolled in the Livongo DPP who completed lesson 1 and recorded a starting weight during 2016–2017. Self-monitoring behaviors, including weigh-ins, food logging, activity, and coach–participant interactions, were analyzed at 6 and 12 months. Subgroup analysis was conducted based on those who were highly engaged versus those minimally engaged. Multiple regression analysis was performed using demographic, self-monitoring, and lesson attendance data to determine predictors of weight loss at 12 months and coaching impact on self-monitoring.ResultsParticipants had a mean age of 50 years (SD ±12), with a starting weight of 94 kg (SD ±21), were college-educated (78%), and were female (74%). Overall, participants lost on average 5.1% of their starting weight. Highly engaged participants lost 6.6% of starting body weight, with 25% losing ≥10% at 12 months. Logistic regression analysis showed each submitted food log was associated with 0.23 kg (p<0.05) weight loss, each lesson completed was associated with 0.14 kg (p<0.05) weight loss, and a week of 150 active minutes was associated with 0.1 kg (p<0.01) weight loss. One additional coach–participant message each week was associated with 1.4 more food logs per week, 1.6% increase in weeks with four or more weigh-ins, and a 2.7% increase in weeks with 150 min of activity.ConclusionsFood logging had the largest impact on weight loss, followed by lesson engagement and physical activity. Future studies should examine further opportunities to deliver nutrition-based content to increase and sustain weight loss for DPP.
The growth in the capabilities of telehealth have made it possible to identify individuals with a higher risk of uncontrolled diabetes and provide them with targeted support and resources to help ...them manage their condition. Thus, predictive modeling has emerged as a valuable tool for the advancement of diabetes management.
This study aimed to conceptualize and develop a novel machine learning (ML) approach to proactively identify participants enrolled in a remote diabetes monitoring program (RDMP) who were at risk of uncontrolled diabetes at 12 months in the program.
Registry data from the Livongo for Diabetes RDMP were used to design separate dynamic predictive ML models to predict participant outcomes at each monthly checkpoint of the participants' program journey (month-n models) from the first day of onboarding (month-0 model) up to the 11th month (month-11 model). A participant's program journey began upon onboarding into the RDMP and monitoring their own blood glucose (BG) levels through the RDMP-provided BG meter. Each participant passed through 12 predicative models through their first year enrolled in the RDMP. Four categories of participant attributes (ie, survey data, BG data, medication fills, and health signals) were used for feature construction. The models were trained using the light gradient boosting machine and underwent hyperparameter tuning. The performance of the models was evaluated using standard metrics, including precision, recall, specificity, the area under the curve, the F
-score, and accuracy.
The ML models exhibited strong performance, accurately identifying observable at-risk participants, with recall ranging from 70% to 94% and precision from 40% to 88% across the 12-month program journey. Unobservable at-risk participants also showed promising performance, with recall ranging from 61% to 82% and precision from 42% to 61%. Overall, model performance improved as participants progressed through their program journey, demonstrating the importance of engagement data in predicting long-term clinical outcomes.
This study explored the Livongo for Diabetes RDMP participants' temporal and static attributes, identification of diabetes management patterns and characteristics, and their relationship to predict diabetes management outcomes. Proactive targeting ML models accurately identified participants at risk of uncontrolled diabetes with a high level of precision that was generalizable through future years within the RDMP. The ability to identify participants who are at risk at various time points throughout the program journey allows for personalized interventions to improve outcomes. This approach offers significant advancements in the feasibility of large-scale implementation in remote monitoring programs and can help prevent uncontrolled glycemic levels and diabetes-related complications. Future research should include the impact of significant changes that can affect a participant's diabetes management.
Technology is rapidly advancing our understanding of how people with diabetes mellitus experience stress.
The aim of this study was to explore the relationship between stress and sequelae of diabetes ...mellitus within a unique data set composed of adults enrolled in a digital diabetes management program, Livongo, in order to inform intervention and product development.
Participants included 3263 adults under age 65 who were diagnosed with diabetes mellitus and had access to Livongo through their employer between June 2015 and August 2018. Data were collected at time of enrollment and 12 months thereafter, which included demographic information, glycemic control, presence of stress, diabetes distress, diabetes empowerment, behavioral health diagnosis, and utilization of behavioral health-related medication and services. Analysis of variance and chi-square tests compared variables across groups that were based on presence of stress and behavioral health diagnosis or utilization.
Fifty-five percent of participants (1808/3263) reported stress at the time of at least 1 blood glucose reading. Fifty-two percent of participants (940/1808) also received at least 1 behavioral health diagnosis or intervention. Compared to their peers, participants with stress reported greater diabetes distress, lower diabetes empowerment, greater insulin use, and poorer glycemic control. Participants with stress and a behavioral health diagnosis/utilization additionally had higher body mass index and duration of illness.
Stress among people with diabetes mellitus is associated with reduced emotional and physical health. Digital products that focus on the whole person by offering both diabetes mellitus self-management tools and behavioral health skills and support can help improve disease-specific and psychosocial outcomes.
To characterize rehabilitation services for patients with knee and hip replacement in 3 types of postacute facilities in the U.S.
Multi-site prospective observational cohort study.
Eight freestanding ...skilled nursing facilities (SNFs), 1 hospital-based SNF, and 11 inpatient rehabilitation facilities (IRFs).
Patients (N=2158) with knee or hip replacement.
No new interventions.
Length of stay (LOS), amount and intensity of physical therapy (PT) and occupational therapy (OT), types of therapy activities.
Average LOS was about 15 days for freestanding SNF patients, and 9 to 10 days for hospital-based SNF and IRF patients. Freestanding SNFs and IRFs provide about the same number of hours of PT and OT; the hospital-based SNF provided 27% fewer hours. Freestanding SNFs and the hospital-based SNF provided fewer hours a day than did IRFs. Joint replacement patients across all 3 types of facilities spent, on average, 70% to 75% of their PT time in just 2 activities--exercise and gait and spent 56% to 66% of their OT time in 3 activities--exercise, functional mobility, and dressing lower body.
Both freestanding SNFs and IRFs provided similar amounts of PT with a similar emphasis on exercise and gait activities. IRFs, however, provided more OT than freestanding SNFs. IRFs had shorter LOSs and more intensive therapy services than freestanding SNFs. Study freestanding SNFs exhibited greater variation in LOS and intensity of therapy than IRFs.
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