scholarly journals Opportunity to inform social needs within a hospital setting using data-driven patient engagement

2021 ◽  
Vol 10 (4) ◽  
pp. e001540
Author(s):  
Shoshana Hahn-Goldberg ◽  
Pauline Pariser ◽  
Colton Schwenk ◽  
Andrew Boozary
Keyword(s):  
High Risk ◽  
Care Delivery ◽  
Hospital Setting ◽  
Social Needs ◽  
Healthcare Use ◽  
High Risk Patients ◽  
Risk Patients ◽  
Using Data ◽  
The Impact ◽  
Current Experience

BackgroundHigh-risk patients account for a disproportionate amount of healthcare use, necessitating the development of care delivery solutions aimed specifically at reducing this use. These interventions have largely been unsuccessful, perhaps due to a lack of attention to patients’ social needs and engagement of patients in developing solutions.MethodsThe project team used a combination of administrative data, information culled from charts and interviews with high-risk patients to understand social needs, the current experience of addressing social needs in the hospital, and patient preferences and identified opportunities for improvement. Interviews were conducted in March and April 2020, and patients were asked to reflect on their experiences both before and during the COVID-19 pandemic.ResultsA total of 4579 patients with 26 168 visits to the emergency department and 2904 inpatient admissions in the previous year were identified. Qualitative analysis resulted in three themes: (1) the interaction between social needs, demographics, and health; (2) the hospital’s role in addressing social needs; and (3) the impact of social needs on experiences of care. Themes related to experiences before and during COVID-19 did not differ. Three opportunities were identified: (1) training for staff related to stigma and trauma, (2) improved documentation of social needs and (3) creation of navigation programmes.DiscussionCertain demographic factors were clearly associated with an increased need for social support. Unfortunately, many factors identified by patients as mediating their need for such support were not consistently captured. Going forward, high-risk patients should be included in the development of quality improvement initiatives and programmes to address social needs.

Abstract 3176: Differerential Impact Of Acute Myocardial Risk Factors And Chronic Comorbidities On Use Of Guideline-recommended Therapies In Patients Presenting With Acute Coronary Syndromes

Circulation ◽  
2008 ◽  
Vol 118 (suppl_18) ◽  
Author(s):  
Karen E Joynt ◽  
Luan Huynh ◽  
John Amarena ◽  
David Brieger ◽  
Steven Coverdale ◽  
...  
Keyword(s):  
Risk Factors ◽  
High Risk ◽  
Artery Bypass ◽  
Severity Of Illness ◽  
Killip Class ◽  
High Risk Patients ◽  
Risk Patients ◽  
Using Data ◽  
The Impact ◽  
To Receive

Background: Prior investigations show that patients presenting with ACS with high-risk features are less likely to receive evidence-based therapies (EBT). Current risk stratification tools combine acute and chronic risk factors (RF); these may contribute differently to receipt of proven therapies. Methods: Using data from a prospective audit of 2600 patients in Australia, the impact of acute myocardial RF (biomarker elevation, ECG changes, cardiac arrest, Killip class ≥ 2), chronic co-morbid RF (prior CHF, chronic lung disease, malignancy, prior CVA, GFR < 60, age > 75), and traditional RF on patient management were examined using logistic regression. Results: the best predictor of receipt of EBT was the number of myocardial RF; the best predictor of a low likelihood of receipt was the number of co-morbid RF (Table ). The presence of three or more myocardial RF conferred an OR of 2.21 for receiving clopidogrel and 4.3 for undergoing angiography, while the presence of four or more co-morbid RF lowered the OR to 0.47 for clopidogrel and 0.09 for angiography (Figure 1a /b ). Conclusions: High-risk patients may be less likely to receive EBT than low-risk patients, but this is driven by comorbid conditions rather than markers of severity of illness. Research is needed to validate these therapies in high-risk patients, and ongoing efforts at quality improvement should focus on high-risk populations. Figure 1a. Odds of receiving clopidogrel during hospitalization Figure 1b. Odds of receiving angiography during hospitalization Application of management recommendations within 24 hours of admission and at discharge, adjusted for cardiovascular risk factors and prior coronary artery bypass grafting.

scholarly journals The impact of the incorporation of a feasible postoperative mortality model at the Post-Anaesthestic Care Unit (PACU) on postoperative clinical deterioration: A pragmatic trial with 5,353 patients

PLoS ONE ◽  
2021 ◽  
Vol 16 (11) ◽  
pp. e0257941
Author(s):  
Claudia de Souza Gutierrez ◽  
Katia Bottega ◽  
Stela Maris de Jezus Castro ◽  
Gabriela Leal Gravina ◽  
Eduardo Kohls Toralles ◽  
...  
Keyword(s):  
Quality Improvement ◽  
High Risk ◽  
Clinical Deterioration ◽  
Surgical Patients ◽  
Predictive Tool ◽  
Postoperative Death ◽  
High Risk Patients ◽  
Death Probability ◽  
Risk Patients ◽  
The Impact

Background Practical use of risk predictive tools and the assessment of their impact on outcome reduction is still a challenge. This pragmatic study of quality improvement (QI) describes the preoperative adoption of a customised postoperative death probability model (SAMPE model) and the evaluation of the impact of a Postoperative Anaesthetic Care Unit (PACU) pathway on the clinical deterioration of high-risk surgical patients. Methods A prospective cohort of 2,533 surgical patients compared with 2,820 historical controls after the adoption of a quality improvement (QI) intervention. We carried out quick postoperative high-risk pathways at PACU when the probability of postoperative death exceeded 5%. As outcome measures, we used the number of rapid response team (RRT) calls within 7 and 30 postoperative days, in-hospital mortality, and non-planned Intensive Care Unit (ICU) admission. Results Not only did the QI succeed in the implementation of a customised risk stratification model, but it also diminished the postoperative deterioration evaluated by RRT calls on very high-risk patients within 30 postoperative days (from 23% before to 14% after the intervention, p = 0.05). We achieved no survival benefits or reduction of non-planned ICU. The small group of high-risk patients (13% of the total) accounted for the highest proportion of RRT calls and postoperative death. Conclusion Employing a risk predictive tool to guide immediate postoperative care may influence postoperative deterioration. It encouraged the design of pragmatic trials focused on feasible, low-technology, and long-term interventions that can be adapted to diverse health systems, especially those that demand more accurate decision making and ask for full engagement in the control of postoperative morbi-mortality.

The Impact of a Postmastectomy Chest Wall Scar Boost on Local Recurrence-free Survival in High-risk Patients

2019 ◽  
Vol 19 (5) ◽  
pp. 363-369
Author(s):  
Ashley Albert ◽  
Sophy Mangana ◽  
Mary R. Nittala ◽  
Toms Vengaloor Thomas ◽  
Lacey Weatherall ◽  
...  
Keyword(s):  
High Risk ◽  
Local Recurrence ◽  
Chest Wall ◽  
Recurrence Free Survival ◽  
Free Survival ◽  
High Risk Patients ◽  
Risk Patients ◽  
The Impact ◽  
Local Recurrence Free Survival

scholarly journals Impact of the Beta-Glucan Test on Management of Intensive Care Unit Patients at Risk for Invasive Candidiasis

2020 ◽  
Vol 58 (6) ◽  
Author(s):  
Antonios Kritikos ◽  
Julien Poissy ◽  
Antony Croxatto ◽  
Pierre-Yves Bochud ◽  
Jean-Luc Pagani ◽  
...  
Keyword(s):  
Intensive Care ◽  
High Risk ◽  
Invasive Candidiasis ◽  
Test Results ◽  
Beta Glucan ◽  
High Risk Patients ◽  
Therapeutic Decisions ◽  
Risk Patients ◽  
The Impact ◽  
Test Result

ABSTRACT The 1,3-beta-d-glucan (BDG) test is used for the diagnosis of invasive candidiasis (IC) in intensive care units (ICUs). However, its utility for patient management is unclear. This study assessed the impact of BDG test results on therapeutic decisions. This was a single-center observational study conducted in an ICU over two 6-month periods. All BDG test requests for the diagnosis of IC were analyzed. Before the second period, the ICU physicians received a pocket card instruction (algorithm) for targeted BDG testing in high-risk patients. The performance of the BDG test for IC diagnosis was assessed, as well as its impact on antifungal (AF) prescription. Overall, 72 patients had ≥1 BDG test, and 14 (19%) patients had an IC diagnosis. The BDG test results influenced therapeutic decisions in 41 (57%) cases. The impact of the BDG test was positive in 30 (73%) of them, as follows: AF abstention/interruption following a negative BDG result (n = 27), and AF initiation/continuation triggered by a positive BDG test result and subsequently confirmed IC (n = 3). In 10 (24%) cases, a positive BDG test result resulted in AF initiation/continuation with no further evidence of IC. A negative BDG result and AF abstention with subsequent IC diagnosis were observed in one case. The positive predictive value (PPV) of BDG was improved if testing was restricted to the algorithm’s indications (80% versus 36%, respectively). However, adherence to the algorithm was low (26%), and no benefit of the intervention was observed. The BDG result had an impact on therapeutic decisions in more than half of the cases, which consisted mainly of safe AF interruption/abstention. Targeted BDG testing in high-risk patients improves PPV but is difficult to achieve in ICU.

scholarly journals 3081 Reducing Reintubation Risk in High-Risk Cardiac Surgery Patients with High Flow Nasal Cannula

2019 ◽  
Vol 3 (s1) ◽  
pp. 29-29
Author(s):  
Robert Edward Freundlich
Keyword(s):  
Risk Factors ◽  
Cardiac Surgery ◽  
High Risk ◽  
Length Of Stay ◽  
Cardiac Surgical Procedures ◽  
High Flow ◽  
High Flow Nasal Cannula ◽  
High Risk Patients ◽  
Risk Patients ◽  
Using Data

OBJECTIVES/SPECIFIC AIMS: More than half a million adult patients nationally undergo cardiac surgery each year. Reintubation following cardiac surgery is common and associated with higher short- and long-term mortality, increased cost, and longer lengths of stay. The reintubation incidence is estimated at 5-10%. Patients undergoing cardiac surgery are increasing in age and comorbidity burden, and receive increasingly complex cardiac surgical procedures, complicating decision making around when to extubate postoperative patients. Compounding this complexity are financial pressures to maintain high throughput and maximize ICU bed availability. Providers are often compelled to extubate high-risk patients earlier, despite the potential for an increased risk of reintubation. Understanding the risk factors for reintubation after cardiac surgery and identifying effective interventions to reduce these reintubations is of critical importance to optimize patient outcomes. High-flow nasal cannula (HFNC) provides up to 60 liters per minute of 100% oxygen, dead space washout, and humidification to improve secretion clearance, and has shown some benefits in improving hypoxia and reducing reintubation in select populations. However, its benefit in high-risk patients undergoing cardiac surgical procedures is not known and therefore clinicians may still be reluctant to extubate these patients early and introduce HFNC, despite the known risks of prolonged intubation. To address this important issue, we aim to develop and validate a model to predict postoperative reintubation after cardiac surgery using data readily available from the electronic health record (EHR) and use this data to complete a pilot randomized controlled trial (RCT) of post-extubation HFNC to prevent reintubation in cardiac surgery patients identified as at high risk for reintubation. METHODS/STUDY POPULATION: Based on retrospective data demonstrating a 4.7% reintubation incidence within 48 hours in our CVICU, we estimate that there will be 340 reintubations available for analysis of the risk factors for reintubation to develop our predictive model from November 2, 2017 (our EHR go-live). We require 15 events per predictive variable to avoid overfitting the model, giving us at least 22 variables for analysis and inclusion in the model. Model validation and calibration will be performed using a bootstrapped validation cohort. Next, we will prospectively study 120 patients with a greater than 10% predicted risk of reintubation (double the baseline risk of the overall population) and randomly assign them to either HFNC or usual care, to test the hypothesis that HFNC decreases the rate of reintubation in high-risk patients. RESULTS/ANTICIPATED RESULTS: In addition to developing a predictive model, refining it, and validating its ability to predict the primary outcome of reintubation within 48 hours, I will further assess whether HFNC reduces total duration of mechanical ventilation, hospital length of stay, and ICU length of stay in this high-risk population. I will use these data to establish the feasibility of EHR-integrated predictive modeling and randomization, as well as to guide a future multicenter clinical trial that will pragmatically leverage the EHR for patient selection, enrollment, randomization, and data collection. DISCUSSION/SIGNIFICANCE OF IMPACT: Assuming HFNC decreases reintubation rates by 50%, at a 1:1 ratio of cases to controls, we will require 435 patients in each group (970 total), to have an 80% power and alpha of 0.05 to detect a difference. As this will require a multicenter study, we will instead focus on using data from this pilot study to: 1) refine our sample size estimates. 2) demonstrate the feasibility of our novel EHR-integrated pragmatic trial design. 3) identify and screen collaborators at other institutions, including obtaining important regulatory and legal approval. 4) establish a data safety monitoring board for the trial. 5) refine the data collection infrastructure, leveraging commercially available resources in one of the largest enterprise EHR systems (Epic) and associated resource-sharing products, such as Epic’s App Orchard.

Survival in Patients with Newly Diagnosed Myeloma Undergoing Therapy with Lenalidomide and Dexamethasone: Impact of High-Risk Cytogenetic Risk Status on Outcome

Blood ◽  
2008 ◽  
Vol 112 (11) ◽  
pp. 95-95 ◽  
Author(s):  
Prashant Kapoor ◽  
Shaji Kumar ◽  
Rafael Fonseca ◽  
Martha Q. Lacy ◽  
Thomas E Witzig ◽  
...  
Keyword(s):  
High Risk ◽  
Risk Stratification ◽  
Plasma Cell ◽  
Risk Groups ◽  
Newly Diagnosed ◽  
High Risk Patients ◽  
Risk Patients ◽  
The Impact ◽  
Standard Risk

Abstract Background: Multiple myeloma (MM) is a heterogeneous disease with very divergent outcomes that are dictated in a large part by specific cytogenetic abnormalities, as well as other prognostic factors such as the proliferative rate of marrow plasma cells. Prognostic systems incorporating these factors have shown clinical utility in identifying high-risk patients, and are increasingly being utilized for treatment decision-making. However, the prognostic relevance of these factors may change with the application of novel therapies. The objective of this study was to determine the impact of risk-stratification (incorporating plasma cell metaphase cytogenetics, interphase fluorescent in-situ hybridization (FISH) and the slide-based plasma cell labeling index (PCLI)) in a cohort of patients with newly diagnosed MM treated initially with lenalidomide + dexamethasone (Rev-Dex). Methods: From March 2004 to November 2007, 100 consecutive patients treated with Rev (25mg/day) on days 1 through 21 of a 4-week cycle in combination with dexamethasone as initial therapy for newly diagnosed myeloma, were identified. High-risk MM was defined as presence of any one or more of the following: hypodiploidy, monoallelic loss of chromosome 13 or its long arm (by metaphase cytogenetics only), deletion of p53 (locus 17p13) or PCLI ≥ 3% or immunoglobulin heavy chain (IgH) translocations, t(4;14) (p16.3;q32) or t(14;16)(q32;q23) on FISH. PFS and OS survival estimates were created using the Kaplan Meier method, and compared by log-rank tests. Results: The median estimated follow-up of the entire cohort (N=100) was 36 months. The median PFS was 31 months; the median OS has not been reached. The 2- and 3-year OS estimates were 93% and 83%, respectively. 16% patients were deemed high-risk by at least one of the 3 tests (cytogenetics, FISH or PCLI). Response rates (PR or better) were 81% versus 89% in the high-risk and standard risk groups, respectively, P=NS; corresponding values for CR plus VGPR rates were 38% and 45% respectively. The median PFS was 18.5 months in high-risk patients compared to 37 months in the standard-risk patients (n=84), P<0.001(Figure). Corresponding values for TTP were 18.5 months and 36.5 months, respectively, P=<0.001. OS was not statistically significant between the two groups; 92% 2-year OS was noted in both the groups. Overall, 95 patients had at least one of the 3 tests to determine risk, while 55 patients could be adequately stratified based on the availability of all the 3 tests, or at least one test result that led to their inclusion in the high-risk category. The significant difference in PFS persisted even when the analysis was restricted to the 55 patients classified using this stringent criterion; 18.5 months vs. 36.5 months in the high-risk and standard- risk groups respectively; P<0.001. In a separate analysis, patients who underwent SCT before the disease progression were censored on the date of SCT to negate its effect, and PFS was still inferior in the high-risk group (p=0.002). Conclusion: The TTP and PFS of high-risk MM patients are inferior to that of the standard-risk patients treated with Rev-Dex, indicating that the current genetic and proliferation-based risk-stratification model remains prognostic with novel therapy. However, the TTP, PFS, and OS obtained in high-risk patients treated with Rev-Dex in this study is comparable to overall results in all myeloma patients reported in recent phase III trials. In addition, no significant impact of high-risk features on OS is apparent so far. Longer follow-up is needed to determine the impact of risk stratification on the OS of patients treated with Rev-Dex. Figure Figure

The Impact of Time of Admission on the Delivery of Care and Outcomes in High Risk Patients with Acute Leukemia

Blood ◽  
2015 ◽  
Vol 126 (23) ◽  
pp. 4530-4530
Author(s):  
Thomas Gregory Knight ◽  
Joshua F. Zeidner ◽  
Naim U Rashid ◽  
Matthew C Foster
Keyword(s):  
High Risk ◽  
Length Of Stay ◽  
Acute Leukemia ◽  
Inpatient Service ◽  
Delivery Of Care ◽  
High Risk Patients ◽  
Short Term Mortality ◽  
Risk Patients ◽  
Time Of Admission ◽  
The Impact

Abstract BACKGROUND: At a large academic teaching hospital, there are a variety of physicians and midlevel providers at the point of initial contact, and the extent of supervision of specifically trained oncology personnel may vary based on time of admission. Patients with acute leukemia may present with high risk disease processes that must be recognized and require prompt intervention to reduce both morbidity and short-term mortality. This is a retrospective review of the delivery of care at admission and key clinical outcomes for high risk patients presenting with acute myeloid leukemia (AML) or acute lymphoblastic leukemia (ALL) based on time of admission. The hypothesis of this study was that high risk patients with AML or ALL admitted overnight may have significant delays in management of the complications of acute leukemia with subsequent increases in morbidity and short-term mortality. METHODS: An institutional electronic database was queried to identify patients with ICD9 codes specific for AML/ALL. Inclusion criteria consisted of adults >18 years admitted to a single institution from 2010-2013. Key clinical data were then abstracted from the electronic medical records including lab values, time of admission (Daytime: 7am-8pm vs Nightime: 8pm-7am), and specific clinically important outcomes (time to specific therapy, time to chemotherapy, length of stay, ICU length of stay, organ failure, and mortality). Patients were categorized as high risk if they met established criteria requiring specific intervention [hyperleukocytosis defined as WBC >50 10^9/L, hyperuricemia defined as uric acid >8 mg/dL, and clinical suspicion for acute promyleocytic leukemia (APL)]. Variables with binary outcomes were tested for association with overnight admission using Fisher's exact test. All other variables were tested using the Wilcoxon two-group test. RESULTS: Between 2010 and 2013, 161 patients with AML/ALL were included in our analysis. Of those, 66 were classified as high risk (Table 1). In the high risk patients there were no significant differences in time to intervention based on time of admission including patients presenting with hyperleukocytosis and time to hydroxyurea administration (p=.32), patients presenting with hyperuricemia and time to allopurinol administration (p=.71) or rasburicase administration (p=.22), and in time to tretinoin (ATRA) administration in patients presenting with APL (p=.23). Time to definitive chemotherapy was significantly less for high risk patients admitted overnight (overnight median=48 hours, day median=56 hours, p=.042). However, rates of mechanical ventilation (p=.09), vasopressor usage (p=.37), and renal failure (p=.43) appeared similar between the groups. Additionally, length of stay (p=.83) and ICU length of stay (p=.44) was not significantly different for the two groups. 30-day mortality did not statistically differ between the two groups (overnight=19.4%, daytime=20%, p=.57). CONCLUSIONS: This is the first comprehensive analysis of the impact of the time of admission of acute leukemia patients at an academic tertiary cancer hospital, to our knowledge. Interestingly, nighttime admissions did not appear to significantly impact time to key clinical interventions or clinical outcomes in high risk patients admitted with acute leukemia. Although time to definitive chemotherapy was found to be significantly less in patients admitted overnight, confounding variables such as severity of illness at the time of admission may have impacted this analysis, and 30-day mortality rates were similar. Overall, this data supports the triage of patients with newly diagnosed or suspected acute leukemia to tertiary care centers as soon as possible. Table 1. Baseline Characteristics of High Risk Patients Age at Diagnosis Number % <50 31 47.0 50-64 24 36.3 65+ 11 16.7 Sex Male 38 57.6 Diagnosis Number % AML (Excluding APL) 37 56.1 APL 18 27.2 ALL 11 16.7 High Risk Features Hyperleukocytosis 42 63.6 Hyperuricemia 20 30.3 APL 18 27.2 >1 High Risk Feature 66 100.0 Initial Point of Contact Number % Referring Hospital 45 68.2 Admission Time Number % Day Shift (7a-8p) 30 45.5 Night Shift (8p-7a) 36 54.5 Admission Location Number % Oncology Inpatient Service 53 80.3 Internal Medicine Inpatient Service 2 3.0 Medical ICU 11 16.7 Disclosures Foster: Celgene: Research Funding.

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