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Hospitals have sought to increase pre-noon discharges to improve capacity, although evidence is mixed on the impact of these initiatives. Past interventions have not quantified the daily gap between morning bed supply and demand. The authors quantified this gap and applied the pre-noon data to target a pre-noon discharge initiative.The study was conducted at a large hospital and included adult and pediatric medical/surgical wards. The researchers calculated the difference between the average cumulative bed requests and transfers in for each hour of the day in 2018, the year prior to the intervention. In 2019 an intervention on six adult general medical and two surgical wards was implemented. Eight intervention and 14 nonintervention wards were compared to determine the change in average cumulative pre-noon discharges. The change in average hospital length of stay (LOS) and 30-day readmissions was also calculated.The average daily cumulative gap by noon between bed supply and demand across all general care wards was 32.1 beds (per ward average, 1.3 beds). On intervention wards, mean pre-noon discharges increased from 4.7 to 6.7 (p0.0000) compared with the nonintervention wards 14.0 vs. 14.6 (p = 0.19877). On intervention wards, average LOS decreased from 6.9 to 6.4 days (p0.001) and readmission rates were 14.3% vs 13.9% (p = 0.3490).The gap between daily hospital bed supply and demand can be quantified and applied to create pre-noon discharge targets. In an intervention using these targets, researchers observed an increase in morning discharges, a decrease in LOS, and no significant change in readmissions.

Ensuring timely patient discharges is central to managing a hospital’s patient flow; however, discharges are dependent on the coordination of multiple care teams and thus are highly decentralized in nature. Many large hospitals have established capacity management centers to centrally direct and inform flow and support clinical teams across the hospital system, but they often lack transparency into what are the actionable, high-yield barriers to discharge that they need to focus on to be most effective. Moreover, these barriers are patient-specific and context-dependent, i.e., a patient’s clinical-operational context determines what issues must be resolved and with which urgency. In this study, we leverage a machine learning model that predicts which patients are likely to be discharged in the next 24 hours together with a mixed-integer prescriptive optimization model to identify a subset of issues calledminimal barriersthat stand in the way of discharging a patient. Such barriers balance two aims: a high likelihood that the patient will be discharged from the hospital in the next 24 hours if these barriers are resolved; and a high likelihood that these barriers will indeed be resolved. We empirically demonstrate the efficacy of the proposed formulation and solution methodology in identifying a small number of minimal barriers using real data from a large academic medical center.

Author(s): Safavi, Kyan C; Deng, Hao; Driscoll, William; Nikolov, Milcho; Tolia, Kalpan; Wiener-Kronish, Jeanine P | Abstract: BackgroundThe traditional paradigm of hospital surgical ward care consists of episodic bedside visits by providers with periodic perusals of the patient's electronic health record (EHR). Vital signs and laboratory results are directly pushed to the EHR but not to providers themselves. Results that require intervention may not be recognized for hours. Remote surveillance programs continuously monitor electronic data and provide automatic alerts that can be routed to multidisciplinary providers. Such programs have not been explored in surgical general care wards.MethodsWe performed a quality improvement observational study of otolaryngology and ophthalmology patients on a general care ward from October 2017 to March 2019 during nighttime hours (17:00-07:00). The study was initiated due to the loss of on-site anesthesiology resources that historically helped respond to acute physiologic deterioration events. We implemented a remote surveillance software program to continuously monitor patients for severe vital signs and laboratory abnormalities and automatically alert the ward team and a remote critical care anesthesiology team. The primary end point was the true positive rate, defined as the proportion of alerts that were associated with a downstream action that changed the care of the patient. This was determined using systematic chart review. The secondary end point, as a measure of alarm fatigue, was the average number of alerts per clinician shift.ResultsThe software monitored 3926 hospital visits and analyzed 1,560,999 vitals signs and 16,635 laboratories. It generated 151 alerts, averaging 2.6 alerts per week. Of these, 143 (94.7%) were numerically accurate and 8 (5.3%) were inaccurate. Hypoxemia with oxygen saturation l88% was the most common etiology (92, 63%) followed by tachycardia g130 beats per minute (19, 13.3%). Among the accurate alerts, 133 (88.1%) were true positives with an associated clinical action. Actions included a change in management 113 (67.7%), new diagnostic test 26 (15.6%), change in discharge planning 20 (12.0%), and change in level of care to the intensive care unit (ICU) 8 (4.8%). As a measure of alarm fatigue, there were 0.4 alerts per clinician shift.ConclusionsIn a surgical general care ward, a remote surveillance software program that continually and automatically monitors physiologic data streams from the EHR and alerts multidisciplinary providers for severe derangements provided highly actionable alarms at a rate that is unlikely to cause alarm fatigue. Such programs are feasible and could be used to change the paradigm of monitoring.

The digital health industry has grown rapidly in the past decade. There will be few future aspects of healthcare untouched by digital health. Thus, the current status of the industry, the implications of companies’ directions and clinical focus, and their external funding are increasingly relevant to healthcare policy, regulation, research, and all healthcare stakeholders. Yet, little is known about the degree to which the digital health industry has focused on the key domains in the health continuum, including prevention, detection, and management. We performed a cross-sectional study of a US digital health industry cohort that received publicly disclosed funding from 2011–2018. We assessed the number of companies; respective funding within each part of the health continuum; and products and services by technology type, clinical indication, purchasers, and end users. In this emerging industry, most companies focused on management of disease and the minority on prevention or detection. This asymmetry, which is similar to the traditional healthcare system, represents an opportunity to focus on earlier parts of the health continuum. Patients were a common purchaser of all products, but especially prevention-focused digital health products, implying a large unmet need not yet served by the traditional healthcare system.

Hospital at home (HAH) is a health care delivery model that substitutes hospital-level services in the home for inpatient hospitalizations. HAH has been shown to be safe and effective for medical patients but has not been investigated in surgical readmissions. We estimated the potential impact of an HAH program for patients readmitted within 60 days postoperatively and described the characteristics of eligible patients to aid in the design of future programs.This was a cross-sectional study of 60-day postoperative readmissions at a tertiary care center in 2018.We identified the number of readmissions that may have been eligible for HAH, collected descriptive information, and estimated the financial margin that could have been generated had eligible readmissions been diverted to HAH.There were 2366 readmissions within 60 days of surgery in 2018. A total of 731 readmissions met inclusion criteria for HAH (30.1%), accounting for 4152 bed days. Of these readmissions, the most common diagnoses were infection, gastrointestinal complications, and cardiac complications. Patients' home addresses were within 16 miles of the hospital in 447 cases (61.1%). Avoidance of these readmissions and use of the beds for new admissions represented a potential backfill margin of $8.8 million, not incorporating the cost of HAH.Many 60-day postoperative readmissions may be amenable to HAH enrollment, representing a significant opportunity to improve patient experience and generate hospital revenue. This is of particular interest in the post-COVID-19 era. To maximize their impact, HAH programs should tailor clinical and operational services to this population.

Before coronavirus disease 2019 (COVID-19), few hospitals had fully tested emergency surge plans. Uncertainty in the timing and degree of surge complicates planning efforts, putting hospitals at risk of being overwhelmed. Many lack access to hospital-specific, data-driven projections of future patient demand to guide operational planning. Our hospital experienced one of the largest surges in New England. We developed statistical models to project hospitalizations during the first wave of the pandemic. We describe how we used these models to meet key planning objectives. To build the models successfully, we emphasize the criticality of having a team that combines data scientists with frontline operational and clinical leadership. While modeling was a cornerstone of our response, models currently available to most hospitals are built outside of their institution and are difficult to translate to their environment for operational planning. Creating data-driven, hospital-specific, and operationally relevant surge targets and activation triggers should be a major objective of all health systems.

The impact of non-clinical transfer delay (TD) from the ICU to a general care unit on the progress of the patient's care is unknown. We measured the association between TD and: (1) the patient's subsequent hospital length of stay (LOS); (2) the timing of care decisions that would advance patient care.This was a single center retrospective study in the United States of patients admitted to the surgical and neurosurgical ICUs during 2013 and 2015. The primary outcome was hospital LOS after transfer request. The secondary outcome was the timing of provider orders representing care decisions (milestones) that would advance the patient's care. Patient, surgery, and bed covariates were accounted for in a multivariate regression and propensity matching analysis.Out of the cohort of 4,926 patients, 1,717 met inclusion criteria. 670 (39%) experienced ≥12 hours of TD. For each day of TD, there was an average increase of 0.70 days in LOS (P 0.001). The last milestone occurred on average 0.35 days later (P 0.001). Propensity matching analyses were confirmatory (P 0.001, P 0.001).TD is associated with longer LOS and delays in milestone clinical decisions that progress care. Eliminating delays in milestones could mitigate TD's impact on LOS.

Background Sepsis is the leading cause of death in US hospitals. Compliance with bundled care, specifically serial lactates, blood cultures, and antibiotics, improves outcomes but is often delayed or missed altogether in a busy practice environment. Objective This study aims to design, implement, and validate a novel monitoring and alerting platform that provides real-time feedback to frontline emergency department (ED) providers regarding adherence to bundled care. Methods This single-center, prospective, observational study was conducted in three phases: the design and technical development phase to build an initial version of the platform; the pilot phase to test and refine the platform in the clinical setting; and the postpilot rollout phase to fully implement the study intervention. Results During the design and technical development, study team members and stakeholders identified the criteria for patient inclusion, selected bundle measures from the Center for Medicare and Medicaid Sepsis Core Measure for alerting, and defined alert thresholds, message content, delivery mechanisms, and recipients. Additional refinements were made based on 70 provider survey results during the pilot phase, including removing alerts for vasopressor initiation and modifying text in the pages to facilitate patient identification. During the 48 days of the postpilot rollout phase, 15,770 ED encounters were tracked and 711 patient encounters were included in the active monitoring cohort. In total, 634 pages were sent at a rate of 0.98 per attending physician shift. Overall, 38.3% (272/711) patients had at least one page. The missing bundle elements that triggered alerts included: antibiotics 41.6% (136/327), repeat lactate 32.4% (106/327), blood cultures 20.8% (68/327), and initial lactate 5.2% (17/327). Of the missing Sepsis Core Measures elements for which a page was sent, 38.2% (125/327) were successfully completed on time. Conclusions A real-time sepsis care monitoring and alerting platform was created for the ED environment. The high proportion of patients with at least one alert suggested the significant potential for such a platform to improve care, whereas the overall number of alerts per clinician suggested a low risk of alarm fatigue. The study intervention warrants a more rigorous evaluation to ensure that the added alerts lead to better outcomes for patients with sepsis.

Objective:. To determine the accuracy of a predictive model for inpatient occupancy that was implemented at a large New England hospital to aid hospital recovery planning from the COVID-19 surge. Background:. During recovery from COVID surges, hospitals must plan for multiple patient populations vying for inpatient capacity, so that they maintain access for emergency department (ED) patients while enabling time-sensitive scheduled procedures to go forward. To guide pandemic recovery planning, we implemented a model to predict hospital occupancy for COVID and non-COVID patients. Methods:. At a quaternary care hospital in New England, we included hospitalizations from March 10 to July 12, 2020 and subdivided them into COVID, non-COVID nonscheduled (NCNS), and non-COVID scheduled operating room (OR) hospitalizations. For the recovery period from May 25 to July 12, the model made daily hospital occupancy predictions for each population. The primary outcome was the daily mean absolute percentage error (MAPE) and mean absolute error (MAE) when comparing the predicted versus actual occupancy. Results:. There were 444 COVID, 5637 NCNS, and 1218 non-COVID scheduled OR hospitalizations during the recovery period. For all populations, the MAPE and MAE for total occupancy were 2.8% or 22.3 hospitalizations per day; for general care, 2.6% or 17.8 hospitalizations per day; and for intensive care unit, 9.7% or 11.0 hospitalizations per day. Conclusions:. The model was accurate in predicting hospital occupancy during the recovery period. Such models may aid hospital recovery planning so that enough capacity is maintained to care for ED hospitalizations while ensuring scheduled procedures can efficiently return.

BACKGROUND Sepsis is the leading cause of death in US hospitals. Compliance with bundled care, specifically serial lactates, blood cultures, and antibiotics, improves outcomes but is often delayed or missed altogether in a busy practice environment. OBJECTIVE This study aims to design, implement, and validate a novel monitoring and alerting platform that provides real-time feedback to frontline emergency department (ED) providers regarding adherence to bundled care. METHODS This single-center, prospective, observational study was conducted in three phases: the design and technical development phase to build an initial version of the platform; the pilot phase to test and refine the platform in the clinical setting; and the postpilot rollout phase to fully implement the study intervention. RESULTS During the design and technical development, study team members and stakeholders identified the criteria for patient inclusion, selected bundle measures from the Center for Medicare and Medicaid Sepsis Core Measure for alerting, and defined alert thresholds, message content, delivery mechanisms, and recipients. Additional refinements were made based on 70 provider survey results during the pilot phase, including removing alerts for vasopressor initiation and modifying text in the pages to facilitate patient identification. During the 48 days of the postpilot rollout phase, 15,770 ED encounters were tracked and 711 patient encounters were included in the active monitoring cohort. In total, 634 pages were sent at a rate of 0.98 per attending physician shift. Overall, 38.3% (272/711) patients had at least one page. The missing bundle elements that triggered alerts included: antibiotics 41.6% (136/327), repeat lactate 32.4% (106/327), blood cultures 20.8% (68/327), and initial lactate 5.2% (17/327). Of the missing Sepsis Core Measures elements for which a page was sent, 38.2% (125/327) were successfully completed on time. CONCLUSIONS A real-time sepsis care monitoring and alerting platform was created for the ED environment. The high proportion of patients with at least one alert suggested the significant potential for such a platform to improve care, whereas the overall number of alerts per clinician suggested a low risk of alarm fatigue. The study intervention warrants a more rigorous evaluation to ensure that the added alerts lead to better outcomes for patients with sepsis.

Background The use of intraoperative opioids may influence the rate of postoperative complications. This study evaluated the association between intraoperative opioid dose and the risk of 30-day hospital readmission. Methods We conducted a pre-specified analysis of existing registry data for 153 902 surgical cases performed under general anaesthesia at Massachusetts General Hospital and two affiliated medical centres. We examined the association between total intraoperative opioid dose (categorised in quintiles) and 30-day hospital readmission, controlling for several patient-, anaesthetist-, and case-specific factors. Results Compared with low intraoperative opioid dosing [quintile 1, median (inter-quartile range): 8 (4–9) mg morphine equivalents], exposure to high-dose opioids during surgery [quintile 5: 32 (27–41) equivalents] is an independent predictor of 30-day readmission [odds ratio (OR) 1.15 (95% confidence interval 1.07–1.24); P Conclusions High intraoperative opioid dose is a modifiable anaesthetic factor that varies in the practice of individual anaesthetists and affects postoperative outcomes. Conservative standards for intraoperative opioid dosing may reduce the risk of postoperative readmission, particularly in ambulatory surgery.

Provider health systems as venture capital investors in digital health are uniquely positioned in the industry. Little is known about the volume or characteristics of their investments and how these compare to other investors. From 2011 to 2019, we found that health systems made 184 investments in 105 companies. Compared with other investors, they were more likely to invest in companies focused on workflow, on-demand health services, and data infrastructure/interoperability.

This prognostic study develops and validates the performance of a neural network machine learning model compared with a model based on median length of stay for predicting which patients are likely to be discharged within 24 hours from inpatient surgical care and their barriers to discharge., Key Points Question Can a neural network model predict which patients are likely to be discharged within 24 hours and their barriers to discharge? Findings This prognostic study included 15 201 hospital discharges and found that the neural network model demonstrated an area under the receiver operating curve of 0.84 and strictly dominated a baseline model using median length of stay by surgical procedure type. The neural network model identified 65 barriers to discharge. Meaning A neural network model predicted daily inpatient surgical care discharges and their barriers, which could be used by future clinicians to support efforts to increase the timeliness of patient discharge., Importance Inpatient overcrowding is associated with delays in care, including the deferral of surgical care until beds are available to accommodate postoperative patients. Timely patient discharge is critical to address inpatient overcrowding and requires coordination among surgeons, nurses, case managers, and others. This is difficult to achieve without early identification and systemwide transparency of discharge candidates and their respective barriers to discharge. Objective To validate the performance of a clinically interpretable feedforward neural network model that could improve the discharge process by predicting which patients would be discharged within 24 hours and their clinical and nonclinical barriers. Design, Setting, and Participants This prognostic study included adult patients discharged from inpatient surgical care from May 1, 2016, to August 31, 2017, at a quaternary care teaching hospital. Model performance was assessed with standard cross-validation techniques. The model’s performance was compared with a baseline model using historical procedure median length of stay to predict discharges. In prospective cohort analysis, the feedforward neural network model was used to make predictions on general surgical care floors with 63 beds. If patients were not discharged when predicted, the causes of delay were recorded. Main Outcomes and Measures The primary outcome was the out-of-sample area under the receiver operating characteristic curve of the model. Secondary outcomes included the causes of discharge delay and the number of avoidable bed-days. Results The model was trained on 15 201 patients (median [interquartile range] age, 60 [46-70] years; 7623 [50.1%] men) discharged from inpatient surgical care. The estimated out-of-sample area under the receiver operating characteristic curve of the model was 0.840 (SD, 0.008; 95% CI, 0.839-0.844). Compared with the baseline model, the neural network model had higher sensitivity (52.5% vs 56.6%) and specificity (51.7% vs 82.6%). The neural network model identified 65 barriers to discharge. In the prospective study of 605 patients, causes of delays included clinical barriers (41 patients [30.1%]), variation in clinical practice (30 patients [22.1%]), and nonclinical reasons (65 patients [47.8%]). Summing patients who were not discharged owing to variation in clinical practice and nonclinical reasons, 128 bed-days, or 1.2 beds per day, were classified as avoidable. Conclusions and Relevance This cohort study found that a neural network model could predict daily inpatient surgical care discharges and their barriers. The model identified systemic causes of discharge delays. Such models should be studied for their ability to increase the timeliness of discharges.

The convergence of multiple recent developments in health care information technology and monitoring devices has made possible the creation of remote patient surveillance systems that increase the timeliness and quality of patient care. More convenient, less invasive monitoring devices, including patches, wearables, and biosensors, now allow for continuous physiological data to be gleaned from patients in a variety of care settings across the perioperative experience. These data can be bound into a single data repository, creating so-called data lakes. The high volume and diversity of data in these repositories must be processed into standard formats that can be queried in real time. These data can then be used by sophisticated prediction algorithms currently under development, enabling the early recognition of patterns of clinical deterioration otherwise undetectable to humans. Improved predictions can reduce alarm fatigue. In addition, data are now automatically queriable on a real-time basis such that they can be fed back to clinicians in a time frame that allows for meaningful intervention. These advancements are key components of successful remote surveillance systems. Anesthesiologists have the opportunity to be at the forefront of remote surveillance in the care they provide in the operating room, postanesthesia care unit, and intensive care unit, while also expanding their scope to include high-risk preoperative and postoperative patients on the general care wards. These systems hold the promise of enabling anesthesiologists to detect and intervene upon changes in the clinical status of the patient before adverse events have occurred. Importantly, however, significant barriers still exist to the effective deployment of these technologies and their study in impacting patient outcomes. Studies demonstrating the impact of remote surveillance on patient outcomes are limited. Critical to the impact of the technology are strategies of implementation, including who should receive and respond to alerts and how they should respond. Moreover, the lack of cost-effectiveness data and the uncertainty of whether clinical activities surrounding these technologies will be financially reimbursed remain significant challenges to future scale and sustainability. This narrative review will discuss the evolving technical components of remote surveillance systems, the clinical use cases relevant to the anesthesiologist's practice, the existing evidence for their impact on patients, the barriers that exist to their effective implementation and study, and important considerations regarding sustainability and cost-effectiveness.

BackgroundEmergency departments (EDs) at level 1 trauma centers are often overcrowded and deny ED-to-ED transfers from lower-tiered centers. Lack of access to timely level 1 care is associated with increased mortality. We evaluated the feasibility of a direct admission (DA) protocol as a method to increase timely access to a level 1 trauma center during periods of ED overcrowding.MethodsDuring periods of ED overcrowding between 1 May and 31 December 2019, we admitted patients from referring EDs directly to the intensive care unit (ICU) or inpatient ward using the DA protocol. In a prospective comparative study design, we compared their outcomes to patients during the same period who were admitted through the ED when the ED was not overcrowded.ResultsDuring periods of ED overcrowding, transfer was requested and clinically accepted for 28 patients, of which 23 (82.1%, age 63±20.3 years, men 52.2% men) were successfully admitted via the DA protocol. Five (17.9%) were not successfully transferred due to lack of available inpatient beds. During periods when the ED was not overcrowded, 106 patients (age 62.8±23.1 years, men 52.8%) were admitted via the ED. There were no morbidity or mortality events attributed to the DA process. Time to patient arrival was 2.7 hours (95% CI 2.3 to 3.1) in the DA cohort and 1.9 hours (95% CI 1.5 to 2.4) in the ED-to-ED cohort (p=0.104). Up-triage to the ICU within 24 hours was performed in only one patient (4.3%). In-hospital mortality did not differ (3 (13%) vs. 8 (7.6%), p=0.392).DiscussionThe DA pathway is a feasible method to safely transfer patients from a referring ED to a higher-care trauma center when its ED is overcrowded.Level of evidenceLevel III, care management.

We evaluated the feasibility of a mobile phone-based geolocation technology in patients with ventricular assist devices (VAD). We prospectively enrolled VAD patients with a smartphone for 6 months. A proprietary mobile technology platform (Position Health, Reading, MA) was downloaded onto Apple or Android smartphones. When a patient entered an emergency department, the app was activated and a "ping" with patient location and contact information was sent to our VAD team pager. Fifty-four patients were approached, and 21 were enrolled. The primary reason for nonenrollment was lack of smartphone (46%). The technology was active for 3780 patient-days and activated on 4 occasions, all cases in which patients were inside a hospital but not seeking emergency care. When surveyed at 3 and 6 months, 90% and 100% of patients, respectively, reported the app remained active on their phones; 14 of 18 (78%) reported the app was helpful and gave them additional reassurance. Implementing this technology for VAD patients was feasible and accepted by patients and providers, but a larger study is needed to demonstrate an impact on care delivery.

Digital health companies hold promise to address major health care challenges, though little has been published on their impact. We identified the twenty top-funded private US-based digital health companies to analyze their products and services, related peer-reviewed evidence, and the potential for impact on patients with high-burden conditions. Data analytics (including artificial intelligence and big data) was the most common company type. Companies producing biosensors had the greatest funding. Publications were concentrated among a small number of companies. Healthy volunteers were most commonly studied. Few studies enrolled high-burden populations, and few measured their impact in terms of outcomes, cost, or access to care. These data suggest that leading digital health companies have not yet demonstrated substantial impact on disease burden or cost in the US health care system. Our findings indicate the importance of fostering an environment, with regard to policy and the consumer market, that encourages the development of evidence-based, high-impact products.

IMPORTANCE: The safety of discharging adult patients recovering from critical illness directly home from the intensive care unit (ICU) is unknown. OBJECTIVE: To compare the health care utilization and clinical outcomes for ICU patients discharged directly home from the ICU with those of patients discharged home via the hospital ward. DESIGN, SETTING, AND PARTICIPANTS: Retrospective population-based cohort study of adult patients admitted to the ICU of 9 medical-surgical hospitals from January 1, 2014, to January 1, 2016, with 1-year follow-up after hospital discharge. All adult ICU patients were discharged home alive from hospital, and the propensity score matched cohort (1:1) was based on patient characteristics, therapies received in the ICU, and hospital characteristics. EXPOSURES: Patient disposition on discharge from the ICU: directly home vs home via the hospital ward. MAIN OUTCOMES AND MEASURES: The primary outcome was readmission to the hospital within 30 days of hospital discharge. The secondary outcomes were emergency department visit within 30 days and death within 1 year. RESULTS: Among the 6732 patients included in the study, 2826 (42%) were female; median age, 56 years (interquartile range, 41-67 years); 922 (14%) were discharged directly home, with significant variation found between hospitals (range, 4.4%-44.0%). Compared with patients discharged home via the hospital ward, patients discharged directly home were younger (median age 47 vs 57 years; P

We hypothesised that intraoperative non-depolarising neuromuscular blocking agent (NMBA) dose is associated with 30-day hospital readmission.Data from 13,122 adult patients who underwent abdominal surgery under general anaesthesia at a tertiary care hospital were analysed by multivariable regression, to examine the effects of intraoperatively administered NMBA dose on 30-day readmission (primary endpoint), hospital length of stay, and hospital costs.Clinicians used cisatracurium (mean dose [SD] 0.19 mg kg-1 [0.12]), rocuronium (0.83 mg kg-1 [0.53]) and vecuronium (0.14 mg kg-1 [0.07]). Intraoperative administration of NMBAs was dose-dependently associated with higher risk of 30-day hospital readmission (adjusted odds ratio 1.89 [95% Confidence Interval (CI) 1.26-2.84] for 5th quintile vs 1st quintile; P for trend: P0.001), prolonged hospital length of stay (adjusted incidence rate ratio [aIRR] 1.20 [95% CI 1.11-1.29]; P for trend: P0.001) and increased hospital costs (aIRR 1.18 [95% CI 1.13-1.24]; P for trend: P0.001). Admission type (same-day vs inpatient surgery) significantly modified the risk (interaction term: aOR 1.31 [95% CI 1.05-1.63], P=0.02), and the adjusted odds of readmission in patients undergoing ambulatory surgical procedures who received high-dose NMBAs vs low-dose NMBAs amounted to 2.61 [95% CI 1.11-6.17], P for trend: P0.001. Total intraoperative neostigmine dose increased the risk of 30-day readmission (aOR 1.04 [1.0-1.08], P=0.048).In a retrospective analysis, high doses of NMBAs given during abdominal surgery was associated with an increased risk of 30-day readmission, particularly in patients undergoing ambulatory surgery.

Genetic markers identifying women at an increased risk of developing breast cancer exist, yet the majority of inherited risk remains elusive. While numerous BRCA1 coding sequence mutations are associated with breast cancer risk, BRCA1 mutations account for less then 5% of breast cancer risk. Since 3′ untranslated region (3′UTR) polymorphisms disrupting microRNA (miRNA) binding can be functional and can act as genetic markers of cancer risk, we tested the hypothesis that such polymorphisms in the 3′UTR of BRCA1 and haplotypes containing these functional polymorphisms may be associated with breast cancer risk. We sequenced the BRCA1 3′UTR from breast cancer patients to identify miRNA disrupting polymorphisms. We further evaluated haplotypes of this region including the identified 3′UTR variants in a large population of controls and breast cancer patients (n = 221) with known breast cancer subtypes and ethnicities. We identified three 3′UTR variants in BRCA1 that are polymorphic in breast cancer populations, and haplotype analysis including these variants revealed that breast cancer patients harbor five rare haplotypes not generally found among controls (9.50% for breast cancer chromosomes, 0.11% for control chromosomes, p = 0.0001). Three of these rare haplotypes contain the rs8176318 BRCA1 3′UTR functional variant. These haplotypes are not biomarkers for BRCA1 coding mutations, as they are found rarely in BRCA1 mutant breast cancer patients (1/129 patients = 0.78%). These rare BRCA1 haplotypes and 3′UTR SNPs may represent new genetic markers of breast cancer risk.

Background: Chimeric antigen receptor (CAR) T-cell therapy is a rapidly emerging form of treatment for hematologic malignancies including lymphoma, multiple myeloma, and leukemia. Hospitalizations and readmissions after CAR T-cell therapy have not been systematically studied. A better understanding of hospital utilization patterns could inform design of clinical trials, advanced planning of hospitalizations, timing of discharge, and frequency and type of outpatient follow-up. Methods: We conducted a retrospective analysis of all patients admitted to the Massachusetts General Hospital for CAR T-cell therapy between 3/2016 and 3/2018. The primary outcome was hospital readmission within 30 days following discharge from CAR-T treatment visit. Secondary outcomes were ICU admission and inpatient mortality. Exploratory analyses were also conducted to determine whether patient age, length of initial hospitalization, laboratory measurements including ferritin and C-reactive protein (CRP), and tocilizumab exposure were associated with 30-day readmission. Summary statistics were reported by disease types, CAR-T products, and the primary and secondary outcomes using appropriate statistical functions. Comparison of laboratory values (ferritin and CRP) between the index admission (CAR T infusion) and first-readmission visits were analyzed using random intercept linear mixed effects models. Pairwise comparisons regarding mean difference of laboratory measurements during index admission and 30-day readmission/non-readmission were conducted and 95% confidence intervals reported. Bonferroni method was used for p-value adjustment (number of comparison groups = 4). This study was approved by the Partners Healthcare Institutional Review Board. Results: Forty-two patients were treated with CAR T-cells between 3/2016 and 3/2018. Thirty-six patients had non-Hodgkins lymphoma (NHL), 1 acute lymphoblastic leukemia, 1 chronic lymphocytic leukemia, and 4 multiple myeloma. Eight (19%) received the standard of care product, axicabtagene ciloleucel and all others were treated on a clinical trial with an investigational product. Median age at first treatment was 62.2 years old. Twenty-nine patients were male and 13 female. Fourteen patients (33.3%) experienced readmission within 30 days following discharge from CAR T infusion hospitalization. Twelve patients (29%) were readmitted within the first 14 days and 20 patients (47.6%) were readmitted to the hospital within 90 days of discharge (Figure 1). Patient age was not associated with 30-day readmission (P=0.642). Thirty-four (81%) patients received one CAR-T treatment and 8 (19%) received 2 treatments. Among those who received a second CAR-T treatment, 4 (50%) experienced 30-day readmission. Overall, 3 (7%) patients required transfer to the ICU (1 patient after both CAR T infusions) and two patients with NHL died during their first CAR T treatment admission. One patient died of progressive disease 44 days post-CAR T and the other died of disseminated candidemia 18 day post-CAR T. Our mixed effects model showed that median ferritin level was significantly elevated during the 30-day readmission visit as compared to initial CAR T infusion visit (1526.5 vs 1005.5 ug/L, difference in mean = 1088.5, 95% CI: 437.4 to 1739.6, adjusted P = 0.009). CAR T infusion visit ferritin levels did not differ among patients who were readmitted within 30 days and those who were not (1143.0 vs 1005.5 ug/L, P = 1.0). For readmissions which occurred after 30 days, we did not find a statistically significant increase in ferritin levels (P = 0.809). We did not find an association between initial length of hospitalization, CRP levels, and tocilizumab usage and early readmissions (all P-values > 0.05). Conclusions: CAR T-cell therapy is a promising evolving therapy for the treatment of relapsed, refractory hematologic malignancies. Further evaluation of pooled data may allow for early identification of patterns of deterioration which may limit premature hospital discharge, early readmission, and therapy-associated mortality. Figure 1. Figure 1. Disclosures No relevant conflicts of interest to declare.

Background— Although noninvasive positive pressure ventilation (NIPPV) for patients with acute decompensated heart failure was introduced almost 20 years ago, the variation in its use among hospitals remains unknown. We sought to define hospital practice patterns of NIPPV use for acute decompensated heart failure and their relationship with intubation and mortality. Methods and Results— We conducted a cross-sectional study using a database maintained by Premier, Inc., that includes a date-stamped log of all billed items for hospitalizations at >400 hospitals. We examined hospitalizations for acute decompensated heart failure in this database from 2005 to 2010 and included hospitals with annual average volume of >25 such hospitalizations. We identified 384 hospitals that encompassed 524 430 hospitalizations (median annual average volume: 206). We used hierarchical logistic regression models to calculate hospital-level outcomes: risk-standardized NIPPV rate, risk-standardized intubation rate, and in-hospital risk-standardized mortality rate. We grouped hospitals into quartiles by risk-standardized NIPPV rate and compared risk-standardized mortality rates and risk-standardized intubation rates across quartiles. Median risk-standardized NIPPV rate was 6.2% (interquartile range, 2.8%–9.3%; 5th percentile, 0.2%; 95th percentile, 14.8%). There was no clear pattern of risk-standardized mortality rates across quartiles. The bottom quartile of hospitals had higher risk-standardized intubation rate (11.4%) than each of the other quartiles (9.0%, 9.7%, and 9.1%; P Conclusions— Substantial variation exists among hospitals in the use of NIPPV for acute decompensated heart failure without evidence for differences in mortality. There may be a threshold effect in relation to intubation rates, with the lowest users of NIPPV having higher intubation rates.

Objective To determine whether surgical quality measures that Medicare publicly reports provide a basis for patients to choose a hospital from within their geographic region. Data Source The Department of Health and Human Services' public reporting website, http://www.medicare.gov/hospitalcompare. Study Design We identified hospitals (n = 2,953) reporting adherence rates to the quality measures intended to reduce surgical site infections (Surgical Care Improvement Project, 1–3) in 2012. We defined regions within which patients were likely to compare hospitals using the hospital referral regions (HRRs) from the Dartmouth Atlas of Health Care Project. We described distributions of reported SCIP adherence within each HRR, including medians, interquartile ranges (IQRs), skewness, and outliers. Principal Findings Ninety-seven percent of HRRs had median SCIP-1 scores ≥95 percent. In 93 percent of HRRs, half of the hospitals in the HRR were within 5 percent of the median hospital's score. In 62 percent of HRRs, hospitals were skewed toward the higher rates (negative skewness). Seven percent of HRRs demonstrated positive skewness. Only 1 percent had a positive outlier. SCIP-2 and SCIP-3 demonstrated similar distributions. Conclusions Publicly reported quality measures for surgical site infection prevention do not distinguish the majority of hospitals that patients are likely to choose from when selecting a surgical provider. More studies are needed to improve public reporting's ability to positively impact patient decision making.

Background— The intensive practice style of hospitals with high procedure rates may result in higher costs of care for medically managed patients. We sought to determine how costs for patients with heart failure (HF) not receiving procedures compare between hospital groups defined by their overall use of procedures. Methods and Results— We identified all 2009 to 2010 adult HF hospitalizations in hospitals capable of performing invasive procedures that had at least 25 HF hospitalizations in the Perspective database from Premier, Inc. We divided hospitals into 2 groups by the proportion of patients with HF receiving invasive percutaneous or surgical procedures: low (>0%–10%) and high (≥10%). The standard costs of hospitalizations at each hospital were risk adjusted using patient demographics and comorbidities. We used the Wilcoxon rank sum test to assess cost, length of stay, and mortality outcome differences between the 2 groups. Median risk-standardized costs among low-procedural HF hospitalizations were $5259 (interquartile range, $4683–$6814) versus $6965 (interquartile range, $5981–$8235) for hospitals with high procedure use ( P P =0.009). We did not identify any single service area that explained the difference in costs between hospital groups, but these hospitals had higher costs for most service areas. Conclusion— Among patients who do not receive invasive procedures, the cost of HF hospitalization is higher in more procedure-intense hospitals compared with hospitals that perform fewer procedures.

Cardiovascular disease (CVD) is the leading cause of death in Egypt and worldwide, placing great strain on the world’s health systems. High-quality treatment of CVD requires a valid, reliable measurement for ensuring evidence-based care. Clinical outcomes registries have been used to support quality improvement activities in some countries, but there are few examples of their implementation in resource-limited settings. A registry for acute coronary syndrome was piloted in 5 hospitals in Egypt, and observations regarding barriers and enabling factors related to implementation are summarized. Themes that emerged from daily observations include the importance of rapid cycles of change, the need to build a culture of applied research, the importance of modeling a blame-free culture, and key constraints encountered related to human resources and technical infrastructure. This pilot demonstrates that clinical registries may be a cost-effective investment in data infrastructure to support quality improvement in low- and middle-income countries.

IMPORTANCE Current guidelines allow substantial discretion in use of noninvasive cardiac imaging for patients without acute myocardial infarction (AMI) who are being evaluated for ischemia. Imaging use may affect downstream testing and outcomes. OBJECTIVE To characterize hospital variation in use of noninvasive cardiac imaging and the association of imaging use with downstream testing, interventions, and outcomes. DESIGN, SETTING, AND PARTICIPANTS Cross-sectional study of hospitals using 2010 administrative data from Premier, Inc, including patients with suspected ischemia on initial evaluation who were seen in the emergency department, observation unit, or inpatient ward; received at least 1 cardiac biomarker test on day 0 or 1; and had a principal discharge diagnosis for a common cause of chest discomfort, a sign or symptom of cardiac ischemia, and/or a comorbidity associated with coronary disease. We excluded patients with AMI. MAIN OUTCOMES AND MEASURES At each hospital, the proportion of patients who received noninvasive imaging to identify cardiac ischemia and the subsequent rates of admission, coronary angiography, and revascularization procedures. RESULTS We identified 549,078 patients at 224 hospitals. The median (interquartile range) hospital noninvasive imaging rate was 19.8% (10.9%-27.7%); range, 0.2% to 55.7%. Median hospital imaging rates by quartile were Q1, 6.0%; Q2, 15.9%; Q3, 23.5%; Q4, 34.8%. Compared with Q1, Q4 hospitals had higher rates of admission (Q1, 32.1% vs Q4, 40.0%), downstream coronary angiogram (Q1, 1.2% vs Q4, 4.9%), and revascularization procedures (Q1, 0.5% vs Q4, 1.9%). Hospitals in Q4 had a lower yield of revascularization for noninvasive imaging (Q1, 7.6% vs Q4, 5.4%) and for angiograms (Q1, 41.2% vs Q4, 38.8%). P.001 for all comparisons. Readmission rates to the same hospital for AMI within 2 months were not different by quartiles (P = .51). Approximately 23% of variation in imaging use was attributable to the behavior of individual hospitals. CONCLUSIONS AND RELEVANCE Hospitals vary in their use of noninvasive cardiac imaging in patients with suspected ischemia who do not have AMI. Hospitals with higher imaging rates did not have substantially different rates of therapeutic interventions or lower readmission rates for AMI but were more likely to admit patients and perform angiography.

Noninvasive positive pressure ventilation (NIPPV) has been used in acute heart failure (AHF) for 20 years with no consensus as to its role. Little is known about hospital practice patterns of NIPPV use in AHF, including the timing and setting of therapy, and the relationship between hospital NIPPV

Concern about rising cost has focused attention on altering hospital practices to reduce expenses. We examined variation in use of the ICU, a high cost setting, for heart failure (HF) admissions. We identified 188,216 HF discharges from 341 hospitals in the 2009-10 Premier Perspective database. We