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Background: Postoperative ileus (POI) after colorectal surgery leads to increased morbidity, costs, and hospital stays. Identifying POI risk for early intervention is important for improving surgical outcomes especially given the increasing trend towards early discharge after surgery. While existing studies have assessed POI risk with regression models, the role of deep learning's remains unexplored. Methods: We assessed the performance and transferability (brutal force/instance/parameter transfer) of Gated Recurrent Unit with Decay (GRU-D), a longitudinal deep learning architecture, for real-time risk assessment of POI among 7,349 colorectal surgeries performed across three hospital sites operated by Mayo Clinic with two electronic health records (EHR) systems. The results were compared with atemporal models on a panel of benchmark metrics. Results: GRU-D exhibits robust transferability across different EHR systems and hospital sites, showing enhanced performance by integrating new measurements, even amid the extreme sparsity of real-world longitudinal data. On average, for labs, vitals, and assisted living status, 72.2%, 26.9%, and 49.3% respectively lack measurements within 24 hours after surgery. Over the follow-up period with 4-hour intervals, 98.7%, 84%, and 95.8% of data points are missing, respectively. A maximum of 5% decrease in AUROC was observed in brutal-force transfer between different EHR systems with non-overlapping surgery date frames. Multi-source instance transfer witnessed the best performance, with a maximum of 2.6% improvement in AUROC over local learning. The significant benefit, however, lies in the reduction of variance (a maximum of 86% decrease). The GRU-D model's performance mainly depends on the prediction task's difficulty, especially the case prevalence rate. Whereas the impact of training data and transfer strategy is less crucial, underscoring the challenge of effectively leveraging transfer learning for rare outcomes. While atemporal Logit models show notably superior performance at certain pre-surgical points, their performance fluctuate significantly and generally underperform GRU-D in post-surgical hours. Conclusion: GRU-D demonstrated robust transferability across EHR systems and hospital sites with highly sparse real-world EHR data. Further research on built-in explainability for meaningful intervention would be highly valuable for its integration into clinical practice.
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BACKGROUND: As primary care populations age, timely identification of palliative care need is becoming increasingly relevant. Previous studies have targeted particular patient populations with life-limiting disease, but few have focused on patients in a primary care setting. Toward this end, we propose a stepped-wedge pragmatic randomized trial whereby a machine learning algorithm identifies patients empaneled to primary care units at Mayo Clinic (Rochester, Minnesota, United States) with high likelihood of palliative care need. METHODS: 42 care team units in 9 clusters were randomized to 7 wedges, each lasting 42 days. For care teams in treatment wedges, palliative care specialists review identified patients, making recommendations to primary care providers when appropriate. Care teams in control wedges receive palliative care under the standard of care. DISCUSSION: This pragmatic trial therefore integrates machine learning into clinical decision making, instead of simply reporting theoretical predictive performance. Such integration has the possibility to decrease time to palliative care, improving patient quality of life and symptom burden. TRIAL REGISTRATION: Clinicaltrials.gov NCT04604457 , restrospectively registered 10/26/2020. PROTOCOL: v0.5, dated 9/23/2020.
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Enfermería de Cuidados Paliativos al Final de la Vida , Cuidados Paliativos , Humanos , Cuidados Paliativos/métodos , Pacientes , Atención Primaria de Salud , Calidad de Vida , Ensayos Clínicos Controlados Aleatorios como Asunto , Ensayos Clínicos Pragmáticos como AsuntoRESUMEN
CONTEXT: Palliative care services are commonly provided to hospitalized patients, but accurately predicting who needs them remains a challenge. OBJECTIVES: To assess the effectiveness on clinical outcomes of an artificial intelligence (AI)/machine learning (ML) decision support tool for predicting patient need for palliative care services in the hospital. METHODS: The study design was a pragmatic, cluster-randomized, stepped-wedge clinical trial in 12 nursing units at two hospitals over a 15-month period between August 19, 2019, and November 17, 2020. Eligible patients were randomly assigned to either a medical service consultation recommendation triggered by an AI/ML tool predicting the need for palliative care services or usual care. The primary outcome was palliative care consultation note. Secondary outcomes included: hospital readmissions, length of stay, transfer to intensive care and palliative care consultation note by unit. RESULTS: A total of 3183 patient hospitalizations were enrolled. Of eligible patients, A total of 2544 patients were randomized to the decision support tool (1212; 48%) and usual care (1332; 52%). Of these, 1717 patients (67%) were retained for analyses. Patients randomized to the intervention had a statistically significant higher incidence rate of palliative care consultation compared to the control group (IRR, 1.44 [95% CI, 1.11-1.92]). Exploratory evidence suggested that the decision support tool group reduced 60-day and 90-day hospital readmissions (OR, 0.75 [95% CI, 0.57, 0.97]) and (OR, 0.72 [95% CI, 0.55-0.93]) respectively. CONCLUSION: A decision support tool integrated into palliative care practice and leveraging AI/ML demonstrated an increased palliative care consultation rate among hospitalized patients and reductions in hospitalizations.
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Inteligencia Artificial , Cuidados Paliativos , Humanos , Hospitalización , Readmisión del Paciente , Derivación y ConsultaRESUMEN
BACKGROUND: Post-surgical complications (PSCs) have been an increasing concern for hospitals in light of Medicare penalties for 30-day readmissions. PSCs have become a target for quality improvement and benchmark for the healthcare system. Over half (60 %) of the deep or organ space surgical site infections are discovered after discharge, leading to a readmission. There has thus been a push to develop risk prediction models for targeted preventive interventions for PSCs. OBJECTIVE: We experiment several Gated Recurrent Unit with Decay (GRU-D) based deep learning architectures with various feature sampling schemes in predicting the risk of colorectal PSCs and compare with atemporal logistic regression models (logit). METHOD: We used electronic health record (EHR) data of 3,535 colorectal surgical patients involved in the national surgical quality improvement program (NSQIP) between 2006 and 2018. Single layer, stacked layer, and multimodal GRU-D models with sigmoid activation were used to develop risk prediction models. Area Under the Receiver Operating Characteristic curve (AUROC) was calculated by comparing predicted probability of developing complications versus truly observed PSCs (NSQIP adjudicated) within 30 days after surgery. We set up cross-validation and an independent held-out dataset for testing model performance consistency. RESULTS AND CONCLUSION: The primary contribution of our study is the formulation of a novel real-time PSC risk prediction task using GRU-D with demonstrated clinical utility. GRU-D outperforms the logit model in predicting wound and organ space infection and shows improved performance as additional data points become available. Logit model outperforms GRU-D before surgery for superficial infection and bleeding. For the same sampling scheme, there is no obvious advantage of complex architectures (stacked, multimodal) over single layer GRU-D. Obtaining more data points closer to the occurrence of PSCs is more important than using a more frequent sampling scheme in training GRU-D models. The fourth predicted risk quartile by single layer GRU-D contains 63 %, 59 %, and 66 % organ space infection cases, at 4 h before, 72 h after, and 168 h after surgery, respectively, suggesting its potential application as a bedside risk assessment tool.
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Neoplasias Colorrectales , Cirugía Colorrectal , Anciano , Humanos , Estados Unidos , Cirugía Colorrectal/efectos adversos , Medicare , Readmisión del Paciente , Infección de la Herida Quirúrgica/epidemiología , Infección de la Herida Quirúrgica/complicaciones , Medición de Riesgo/métodos , Complicaciones Posoperatorias/epidemiología , Complicaciones Posoperatorias/etiología , Estudios RetrospectivosRESUMEN
OBJECTIVE: Hospitalized patients discharged to skilled nursing facilities (SNFs) for post-acute care are at high risk for adverse outcomes. Yet, absence of effective prognostic tools hinders optimal care planning and decision making. Our objective was to develop and validate a risk prediction model for 6-month all-cause death among hospitalized patients discharged to SNFs. DESIGN: Retrospective cohort study. SETTING AND PARTICIPANTS: Patients discharged from 1 of 2 hospitals to 1 of 10 SNFs for post-acute care in an integrated health care delivery system between January 1, 2009, and December 31, 2016. METHODS: Gradient-boosting machine modeling was used to predict all-cause death within 180 days of hospital discharge with use of patient demographic characteristics, comorbidities, pattern of prior health care use, and clinical parameters from the index hospitalization. Area under the receiver operating characteristic curve (AUC) was assessed for out-of-sample observations under 10-fold cross-validation. RESULTS: We identified 9803 unique patients with 11,647 hospital-to-SNF discharges [mean (SD) age, 80.72 (9.71) years; female sex, 61.4%]. These discharges involved 9803 patients alive at 180 days and 1844 patients who died between day 1 and day 180 of discharge. Age, comorbid burden, health care use in prior 6 months, abnormal laboratory parameters, and mobility status during hospital stay were the most important predictors of 6-month death (model AUC, 0.82). CONCLUSION AND IMPLICATIONS: We derived a robust prediction model with parameters available at discharge to SNFs to calculate risk of death within 6 months. This work may be useful to guide other clinicians wishing to develop mortality prediction instruments specific to their post-acute SNF populations.
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Alta del Paciente , Instituciones de Cuidados Especializados de Enfermería , Anciano de 80 o más Años , Femenino , Humanos , Lactante , Readmisión del Paciente , Estudios Retrospectivos , Atención Subaguda , Estados UnidosRESUMEN
We followed 106â 349 primary care patients for 22â 385 3099 person-days across 21 calendar months and documented 69 breakthrough coronavirus disease 2019 (COVID-19) hospitalizations: 65/102,613 (0.06%) among those fully vaccinated, 3/11â 047 (0.03%) among those previously infected, and 1/7,313 (0.01%) among those with both statuses. These data give providers real-world context regarding breakthrough COVID-19 hospitalization risk.
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COVID-19 , COVID-19/prevención & control , Hospitalización , Humanos , Incidencia , Atención Primaria de Salud , VacunaciónRESUMEN
BACKGROUND: Palliative care is a medical specialty centered on improving the quality of life (QOL) of patients with complex or life-threatening illnesses. The need for palliative care is increasing and with that the rigorous testing of triage tools that can be used quickly and reliably to identify patients that may benefit from palliative care. METHODS: To that aim, we will conduct a two-armed stepped-wedge cluster randomized trial rolled out to two inpatient hospitals to evaluate whether a machine learning algorithm accurately identifies patients who may benefit from a comprehensive review by a palliative care specialist and decreases time to receiving a palliative care consult in hospital. This is a single-center study which will be conducted from August 2019 to November 2020 at Saint Mary's Hospital & Methodist Hospital both within Mayo Clinic Rochester in Minnesota. Clusters will be nursing units which will be chosen to be a mix of complex patients from Cardiology, Critical Care, and Oncology and had previously established relationships with palliative medicine. The stepped wedge design will have 12 units allocated to a design matrix of 5 treatment wedges. Each wedge will last 75 days resulting in a study period of 12 months of recruitment unless otherwise specified. Data will be analyzed with Bayesian hierarchical models with credible intervals denoting statistical significance. DISCUSSION: This intervention offers a pragmatic approach to delivering specialty palliative care to hospital patients in need using machine learning, thereby leading to high value care and improved outcomes. It is not enough for AI to be utilized by simply publishing research showing predictive performance; clinical trials demonstrating better outcomes are critically needed. Furthermore, the deployment of an AI algorithm is a complex process that requires multiple teams with varying skill sets. To evaluate a deployed AI, a pragmatic clinical trial can accommodate the difficulties of clinical practice while retaining scientific rigor. TRIAL REGISTRATION: ClinicalTrials.gov NCT03976297 . Registered on 6 June 2019, prior to trial start.
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Cuidados Paliativos , Calidad de Vida , Adulto , Teorema de Bayes , Humanos , Pacientes Internos , Oncología Médica , Ensayos Clínicos Controlados Aleatorios como Asunto , Literatura de Revisión como AsuntoRESUMEN
Predictive models have played a critical role in local, national, and international response to the COVID-19 pandemic. In the United States, health care systems and governmental agencies have relied on several models, such as the Institute for Health Metrics and Evaluation, Youyang Gu (YYG), Massachusetts Institute of Technology, and Centers for Disease Control and Prevention ensemble, to predict short- and long-term trends in disease activity. The Mayo Clinic Bayesian SIR model, recently made publicly available, has informed Mayo Clinic practice leadership at all sites across the United States and has been shared with Minnesota governmental leadership to help inform critical decisions during the past year. One key to the accuracy of the Mayo Clinic model is its ability to adapt to the constantly changing dynamics of the pandemic and uncertainties of human behavior, such as changes in the rate of contact among the population over time and by geographic location and now new virus variants. The Mayo Clinic model can also be used to forecast COVID-19 trends in different hypothetical worlds in which no vaccine is available, vaccinations are no longer being accepted from this point forward, and 75% of the population is already vaccinated. Surveys indicate that half of American adults are hesitant to receive a COVID-19 vaccine, and lack of understanding of the benefits of vaccination is an important barrier to use. The focus of this paper is to illustrate the stark contrast between these 3 scenarios and to demonstrate, mathematically, the benefit of high vaccine uptake on the future course of the pandemic.
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Vacunas contra la COVID-19 , COVID-19/prevención & control , COVID-19/epidemiología , Predicción , Hospitalización/estadística & datos numéricos , Hospitalización/tendencias , Humanos , Estados Unidos/epidemiologíaRESUMEN
In March 2020, our institution developed an interdisciplinary predictive analytics task force to provide coronavirus disease 2019 (COVID-19) hospital census forecasting to help clinical leaders understand the potential impacts on hospital operations. As the situation unfolded into a pandemic, our task force provided predictive insights through a structured set of visualizations and key messages that have helped the practice to anticipate and react to changing operational needs and opportunities. The framework shared here for the deployment of a COVID-19 predictive analytics task force could be adapted for effective implementation at other institutions to provide evidence-based messaging for operational decision-making. For hospitals without such a structure, immediate consideration may be warranted in light of the devastating COVID-19 third-wave which has arrived for winter 2020-2021.
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COVID-19/terapia , Toma de Decisiones , Manejo de la Enfermedad , Hospitales/estadística & datos numéricos , Unidades de Cuidados Intensivos/estadística & datos numéricos , Pandemias , SARS-CoV-2 , COVID-19/epidemiología , Predicción , HumanosRESUMEN
OBJECTIVE: Access to palliative care (PC) is important for many patients with uncontrolled symptom burden from serious or complex illness. However, many patients who could benefit from PC do not receive it early enough or at all. We sought to address this problem by building a predictive model into a comprehensive clinical framework with the aims to (i) identify in-hospital patients likely to benefit from a PC consult, and (ii) intervene on such patients by contacting their care team. MATERIALS AND METHODS: Electronic health record data for 68 349 inpatient encounters in 2017 at a large hospital were used to train a model to predict the need for PC consult. This model was published as a web service, connected to institutional data pipelines, and consumed by a downstream display application monitored by the PC team. For those patients that the PC team deems appropriate, a team member then contacts the patient's corresponding care team. RESULTS: Training performance AUC based on a 20% holdout validation set was 0.90. The most influential variables were previous palliative care, hospital unit, Albumin, Troponin, and metastatic cancer. The model has been successfully integrated into the clinical workflow making real-time predictions on hundreds of patients per day. The model had an "in-production" AUC of 0.91. A clinical trial is currently underway to assess the effect on clinical outcomes. CONCLUSIONS: A machine learning model can effectively predict the need for an inpatient PC consult and has been successfully integrated into practice to refer new patients to PC.
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Aprendizaje Automático , Informática Médica , Cuidados Paliativos , Anciano , Área Bajo la Curva , Sistemas de Apoyo a Decisiones Clínicas , Atención a la Salud , Registros Electrónicos de Salud , Femenino , Humanos , Masculino , Persona de Mediana Edad , Mejoramiento de la Calidad , Curva ROCRESUMEN
OBJECTIVE: Anemia is common during critical illness and often persists after hospital discharge; however, its potential association with physical outcomes after critical illness is unclear. Our objective was to assess the associations between hemoglobin at intensive care unit (ICU) and hospital discharge with physical status at 3-month follow-up in acute respiratory distress syndrome (ARDS) survivors. METHODS: This is a secondary analysis of a multisite prospective cohort study of 195 mechanically ventilated ARDS survivors from 13 ICUs at 4 teaching hospitals in Baltimore, Maryland. Multivariable regression was utilized to assess the relationships between ICU and hospital discharge hemoglobin concentrations with measures of physical status at 3 months, including muscle strength (Medical Research Council sumscore), exercise capacity (6-minute walk distance [6MWD]), and self-reported physical functioning (36-Item Short-Form Health Survey [SF-36v2] Physical Function score and Activities of Daily Living [ADL] dependencies). RESULTS: Median (interquartile range) hemoglobin concentrations at ICU and hospital discharge were 9.5 (8.5-10.7) and 10.0 (9.0-11.2) g/dL, respectively. In multivariable regression analyses, higher ICU discharge hemoglobin concentrations (per 1 g/dL) were associated with greater 3-month 6MWD mean percent of predicted (3.7% [95% confidence interval 0.8%-6.5%]; P = .01) and fewer ADL dependencies (-0.2 [-0.4 to -0.1]; P = .02), but not with percentage of maximal muscle strength (0.7% [-0.9 to 2.3]; P = .37) or SF-36v2 normalized Physical Function scores (0.8 [-0.3 to 1.9]; P = .15). The associations of physical outcomes and hospital discharge hemoglobin concentrations were qualitatively similar, but none were statistically significant. CONCLUSIONS: In ARDS survivors, higher hemoglobin concentrations at ICU discharge, but not hospital discharge, were significantly associated with improved exercise capacity and fewer ADL dependencies. Future studies are warranted to further assess these relationships.
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Anemia , Síndrome de Dificultad Respiratoria , Actividades Cotidianas , Enfermedad Crítica , Humanos , Unidades de Cuidados Intensivos , Estudios Prospectivos , Síndrome de Dificultad Respiratoria/terapiaRESUMEN
Background: We developed a novel analytic tool for colorectal deep organ/space surgical site infections (C-OSI) prediction utilizing both institutional and extra-institutional American College of Surgeons-National Surgical Quality Improvement Program (ACS-NSQIP) data. Methods: Elective colorectal resections (2006-2014) were included. The primary end point was C-OSI rate. A Bayesian-Probit regression model with multiple imputation (BPMI) via Dirichlet process handled missing data. The baseline model for comparison was a multivariable logistic regression model (generalized linear model; GLM) with indicator parameters for missing data and stepwise variable selection. Out-of-sample performance was evaluated with receiver operating characteristic (ROC) analysis of 10-fold cross-validated samples. Results: Among 2,376 resections, C-OSI rate was 4.6% (n = 108). The BPMI model identified (n = 57; 56% sensitivity) of these patients, when set at a threshold leading to 80% specificity (approximately a 20% false alarm rate). The BPMI model produced an area under the curve (AUC) = 0.78 via 10-fold cross- validation demonstrating high predictive accuracy. In contrast, the traditional GLM approach produced an AUC = 0.71 and a corresponding sensitivity of 0.47 at 80% specificity, both of which were statstically significant differences. In addition, when the model was built utilizing extra-institutional data via inclusion of all (non-Mayo Clinic) patients in ACS-NSQIP, C-OSI prediction was less accurate with AUC = 0.74 and sensitivity of 0.47 (i.e., a 19% relative performance decrease) when applied to patients at our institution. Conclusions: Although the statistical methodology associated with the BPMI model provides advantages over conventional handling of missing data, the tool should be built with data specific to the individual institution to optimize performance.
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Infección de la Herida Quirúrgica , Área Bajo la Curva , Teorema de Bayes , Humanos , Modelos Logísticos , Curva ROC , Medición de Riesgo , Infección de la Herida Quirúrgica/epidemiologíaRESUMEN
OBJECTIVES: Older patients discharged to skilled nursing facilities (SNFs) for post-acute care are at high risk for hospital readmission. Yet, as in the community setting, some readmissions may be preventable with optimal transitional care. This study examined the proportion of 30-day hospital readmissions from SNFs that could be considered potentially preventable readmissions (PPRs) and evaluated the reasons for these readmissions. DESIGN: Retrospective cohort study. SETTING AND PARTICIPANTS: Post-acute practice of an integrated health care delivery system serving 11 SNFs in the US Midwest. Patients discharged from the hospital to an SNF and subsequently readmitted to the hospital within 30 days from January 1, 2009, through November 31, 2016. METHODS: A computerized algorithm evaluated the relationship between initial and repeat hospitalizations to determine whether the repeat hospitalization was a PPR. We assessed for changes in PPR rates across the system over the study period and evaluated the readmission categories to identify the most prevalent PPR categories. RESULTS: Of 11,976 discharges to SNFs for post-acute care among 8041 patients over the study period, 16.6% resulted in rehospitalization within 30 days, and 64.8% of these rehospitalizations were considered PPRs. Annual proportion of PPRs ranged from 58.2% to 66.4% [mean (standard deviation) 0.65 (0.03); 95% confidence interval CI 0.63-0.67; P = .36], with no discernable trend. Nearly one-half (46.2%) of all 30-day readmissions were classified as potentially preventable medical readmissions related to recurrence or continuation of the reason for initial admission or to complications from the initial hospitalization. CONCLUSIONS AND IMPLICATIONS: For this cohort of patients discharged to SNFs, a computerized algorithm categorized a large proportion of 30-day hospital readmissions as potentially preventable, with nearly one-half of those linked to the reason for the initial hospitalization. These findings indicate the importance of improvement in postdischarge transitional care for patients discharged to SNFs.
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Readmisión del Paciente , Instituciones de Cuidados Especializados de Enfermería , Cuidados Posteriores , Algoritmos , Humanos , Alta del Paciente , Estudios Retrospectivos , Estados UnidosRESUMEN
Importance: Anemia is common and has been associated with poor outcomes in the critically ill population, yet the timing and extent of hemoglobin recovery remains incompletely described, which may have important implications for clinical outcomes following discharge from intensive care. Objectives: To describe longitudinal changes in anemia status during and after critical illness and assess the associations between hemoglobin concentrations and postdischarge mortality. Design, Setting, and Participants: A population-based cohort study was conducted from January 1, 2010, to December 31, 2016, in Olmsted County, Minnesota; data analysis was performed from June 1 to December 30, 2019. Participants included 6901 adults (age ≥18 years) admitted to intensive care. Main Outcomes and Measures: Hemoglobin concentrations in the 12 months before hospitalization, during hospitalization, and in the 12 months after discharge, categorized by anemia severity (mild, hemoglobin ≥10.0 to <12.0 g/dL in women or ≥10.0 to <13.5 g/dL in men; moderate, hemoglobin ≥8.0 to <10.0 g/dL; and severe, hemoglobin <8.0 g/dL). Complete recovery from anemia, defined as attainment of nonanemic status by 12 months post hospitalization, and 12-month mortality were also evaluated. Results: Of the 6901 patients included in the study, 3792 were men (55%); median (interquartile range [IQR]) age was 67 (IQR, 52-79) years. Prehospitalization hemoglobin concentrations were available in 83% of the population (n = 5694), with median hemoglobin concentrations of 13.1 (IQR, 11.6-14.4) g/dL. Forty-one percent of the patients (n = 2320) had anemia preceding hospitalization. Hemoglobin values at hospital discharge were 10.8 g/dL (IQR, 9.5-12.4 g/dL), with 80% (n = 5182 of 6460) having anemia: 58% mild, 39% moderate, and 3% severe. The prevalence of anemia post hospitalization was 56% (95% CI, 55%-58%) at 3 months, 52% (95% CI, 50%-54%) at 6 months, and 45% (95% CI, 43%-47%) at 12 months among those alive with available hemoglobin measurements. Rates of complete recovery from anemia at 12 months were 58% (95% CI, 56%-61%) for mild anemia, 39% (95% CI, 36%-42%) for moderate anemia, and 24% (95% CI, 15%-34%) for severe anemia. Of those without baseline anemia surviving hospitalization, 74% of the patients were anemic at hospital discharge, with rates of complete 12-month recovery of 73% (95% CI, 69%-76%) for mild anemia, 62% (95% CI, 57%-68%) for moderate anemia, and 59% (95% CI, 35%-82%) for severe anemia. Higher hospital discharge hemoglobin concentrations were associated with decreased mortality after multivariable adjustment (hazard ratio, 0.95 per 1-g/dL increase; 95% CI, 0.90-0.99, P = .02). Conclusions and Relevance: The findings of this study suggest that anemia is common and often persistent in the first year after critical illness. Further studies are warranted to identify distinct anemia recovery profiles and assess associations with clinical outcomes.
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Cuidados Posteriores/estadística & datos numéricos , Anemia/epidemiología , Hospitalización/estadística & datos numéricos , Adolescente , Adulto , Anemia/etiología , Resultados de Cuidados Críticos , Enfermedad Crítica , Femenino , Hemoglobinas/análisis , Humanos , Estudios Longitudinales , Masculino , Minnesota/epidemiología , Prevalencia , Modelos de Riesgos Proporcionales , Adulto JovenRESUMEN
BACKGROUND: The prevalence of inadequate symptom control among cancer patients is quite high despite the availability of definitive care guidelines and accurate and efficient assessment tools. METHODS: We will conduct a hybrid type 2 stepped wedge pragmatic cluster randomized clinical trial to evaluate a guideline-informed enhanced, electronic health record (EHR)-facilitated cancer symptom control (E2C2) care model. Teams of clinicians at five hospitals that care for patients with various cancers will be randomly assigned in steps to the E2C2 intervention. The E2C2 intervention will have two levels of care: level 1 will offer low-touch, automated self-management support for patients reporting moderate sleep disturbance, pain, anxiety, depression, and energy deficit symptoms or limitations in physical function (or both). Level 2 will offer nurse-managed collaborative care for patients reporting more intense (severe) symptoms or functional limitations (or both). By surveying and interviewing clinical staff, we will also evaluate whether the use of a multifaceted, evidence-based implementation strategy to support adoption and use of the E2C2 technologies improves patient and clinical outcomes. Finally, we will conduct a mixed methods evaluation to identify disparities in the adoption and implementation of the E2C2 intervention among elderly and rural-dwelling patients with cancer. DISCUSSION: The E2C2 intervention offers a pragmatic, scalable approach to delivering guideline-based symptom and function management for cancer patients. Since discrete EHR-imbedded algorithms drive defining aspects of the intervention, the approach can be efficiently disseminated and updated by specifying and modifying these centralized EHR algorithms. TRIAL REGISTRATION: ClinicalTrials.gov, NCT03892967. Registered on 25 March 2019.
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Registros Electrónicos de Salud , Oncología Médica/métodos , Cuidados Paliativos/métodos , Grupo de Atención al Paciente , Análisis por Conglomerados , Humanos , Informática Médica/métodos , Oncología Médica/normas , Estudios Multicéntricos como Asunto , Medición de Resultados Informados por el Paciente , Ensayos Clínicos Pragmáticos como Asunto , Calidad de Vida , AutomanejoRESUMEN
Skilled nursing facilities (SNFs) increasingly provide care to patients after hospitalization. The Centers for Medicare & Medicaid Services reports ratings for SNFs for overall quality, staffing, health inspections, and clinical quality measures. However, the relationship between these ratings and patient outcomes remains unclear. In this retrospective cohort study, we reviewed the electronic health records of 3,923 adult patients discharged from the hospital and admitted to 9 SNFs served by a health care delivery system. We used Cox proportional hazards models to examine associations between the overall quality and individual ratings and our primary outcomes of 30-day rehospitalizations and 30-day emergency department visits. Patients in higher-rated facilities had a 13% lower risk of 30-day rehospitalization than patients in lower-rated facilities (hazard ratio, 0.87; 95% CI, 0.76-0.99). The risk of emergency department visits was also lower for patients in facilities with a higher overall quality rating and a higher quality measures rating. Staffing and health inspection ratings were not associated with our primary outcomes. These findings may help inform providers and nursing home policy makers.
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The equations of a physical constitutive model for material stress within tantalum grains were solved numerically using a tetrahedrally meshed volume. The resulting output included a scalar vonMises stress for each of the more than 94,000 tetrahedra within the finite element discretization. In this paper, we define an intricate statistical model for the spatial field of vonMises stress which uses the given grain geometry in a fundamental way. Our model relates the three-dimensional field to integrals of latent stochastic processes defined on the vertices of the one- and two-dimensional grain boundaries. An intuitive neighborhood structure of the said boundary nodes suggested the use of a latent Gaussian Markov random field (GMRF). However, despite the potential for computational gains afforded by GMRFs, the integral nature of our model and the sheer number of data points pose substantial challenges for a full Bayesian analysis. To overcome these problems and encourage efficient exploration of the posterior distribution, a number of techniques are now combined: parallel computing, sparse matrix methods, and a modification of a block update strategy within the sampling routine. In addition, we use an auxiliary variables approach to accommodate the presence of outliers in the data.
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BACKGROUND: Little information exists regarding the use of arch operations for repair of acute type A aortic dissections (AADs) despite increasing interest in this strategy and its potential impact on outcomes. We aimed to determine the relationship between extent of aortic repair, US geographic regions, and outcome. METHODS: We queried The Society of Thoracic Surgeons database for patients who underwent AAD repair from January 1, 2004 to December 31, 2016 and grouped patients by ascending-only operations and operations involving the arch. RESULTS: We identified 25,462 patients (mean age, 59.8 ± 14.2; 66.7% men) who underwent AAD repair. Operations involving the ascending aorta only were performed in 54% of patients; 46% had repair additionally involving the arch. The 30-day mortality was 18.9% for patients who underwent ascending-only operations vs 19.8% for patients who underwent arch operations (P = .09). In multivariable analysis older age (P < .001), earlier year of operation (P < .001), diabetes mellitus (P < .001), severe chronic lung disease (P < .001), prior cerebrovascular disease (P < .001), and longer bypass time (P < .001) were independently associated with 30-day mortality. There was regional variation in 30-day mortality (P < .001), and incidence of arch repair varied from 38.6% to 52.6% in 9 geographic regions (P < .001). CONCLUSIONS: In this analysis of cardiac surgical practice in the United States, repair of AADs included a portion of the aortic arch in 46% of patients. Early mortality remained high throughout the current era regardless of extent of aortic resection. Regional variation in perioperative mortality may signal an opportunity for practice improvement.
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Enfermedades de la Aorta/cirugía , Disección Aórtica/cirugía , Enfermedad Aguda , Anciano , Disección Aórtica/clasificación , Enfermedades de la Aorta/clasificación , Femenino , Humanos , Masculino , Persona de Mediana Edad , Factores de Tiempo , Resultado del Tratamiento , Estados Unidos , Procedimientos Quirúrgicos Vasculares/métodosRESUMEN
In recent years, there is increasing enthusiasm in the healthcare research community for artificial intelligence to provide big data analytics and augment decision making. One of the prime reasons for this is the enormous impact of deep learning for utilization of complex healthcare big data. Although deep learning is a powerful analytic tool for the complex data contained in electronic health records (EHRs), there are also limitations which can make the choice of deep learning inferior in some healthcare applications. In this paper, we give a brief overview of the limitations of deep learning illustrated through case studies done over the years aiming to promote the consideration of alternative analytic strategies for healthcare.
RESUMEN
OBJECTIVES: Patients discharged to a skilled nursing facility (SNF) for post-acute care have a high risk of hospital readmission. We aimed to develop and validate a risk-prediction model to prospectively quantify the risk of 30-day hospital readmission at the time of discharge to a SNF. DESIGN: Retrospective cohort study. SETTING: Ten independent SNFs affiliated with the post-acute care practice of an integrated health care delivery system. PARTICIPANTS: We evaluated 6032 patients who were discharged to SNFs for post-acute care after hospitalization. MEASUREMENTS: The primary outcome was all-cause 30-day hospital readmission. Patient demographics, medical comorbidity, prior use of health care, and clinical parameters during the index hospitalization were analyzed by using gradient boosting machine multivariable analysis to build a predictive model for 30-day hospital readmission. Area under the receiver operating characteristic curve (AUC) was assessed on out-of-sample observations under 10-fold cross-validation. RESULTS: Among 8616 discharges to SNFs from January 1, 2009, through June 30, 2014, a total of 1568 (18.2%) were readmitted to the hospital within 30 days. The 30-day hospital readmission prediction model had an AUC of 0.69, a 16% improvement over risk assessment using the Charlson Comorbidity Index alone. The final model included length of stay, abnormal laboratory parameters, and need for intensive care during the index hospitalization; comorbid status; and number of emergency department and hospital visits within the preceding 6 months. CONCLUSIONS AND IMPLICATIONS: We developed and validated a risk-prediction model for 30-day hospital readmission in patients discharged to a SNF for post-acute care. This prediction tool can be used to risk stratify the complex population of hospitalized patients who are discharged to SNFs to prioritize interventions and potentially improve the quality, safety, and cost-effectiveness of care.