Risk-adjusting hospital mortality using a comprehensive electronic record in an integrated health care delivery system

Risk classification for long-term mortality among patients with acute heart failure: China PEACE 4YMortality

AIMS

There are limited tools to predict long-term mortality among patients hospitalized with acute heart failure (AHF) in China. This study aimed to develop and validate a model to predict long-term mortality risk among patients who were hospitalized with AHF and discharged alive.

METHODS

We used data from China Patient-Centred Evaluative Assessment of Cardiac Events Prospective Heart Failure Study. Multivariate Cox proportional hazard model was used to develop and internal validate a model to predict 4 year mortality risk.

RESULTS

The study included 4875 patients hospitalized for AHF, of whom 2066 (42.38%) died within 4 years following admission, with a median survival time of 3.91 (interquartile range: 1.67, 4.00) years. We selected 13 predictors to establish the model, including age, medical history of hypertension, chronic obstructive pulmonary disease and HF, systolic blood pressure, blood urea nitrogen, albumin, high-sensitivity troponin T, N-terminal pro-brain natriuretic peptide, serum creatine, Kansas City Cardiomyopathy Questionnaire-12 score and left ventricular ejection fraction. The model showed a reasonable performance with the discrimination [C-index was 0.726 (95% confidence interval, CI: 0.714, 0.739) in the development cohort and 0.727 (95% CI: 0.708, 0.747) in the validation cohort]. We then built a point-based risk score algorithm and the patients were stratified to low-risk (0-14), intermediate-risk (15-19) and high-risk (≥20) groups.

CONCLUSIONS

By using readily accessible predictors, we developed and validated a risk prediction model to predict 4 year mortality risk among patients who were hospitalized with AHF and discharged alive. This model proved beneficial for individual risk stratification and facilitating ongoing enhancements in patient outcomes.

Effects of the National Institutes of Health Stroke Scale and Modified Rankin Scale on Predictive Models of 30-Day Nonelective Readmission and Mortality After Ischemic Stroke: Cohort Study

Background

Patients with stroke have high rates of all-cause readmission and case fatality. Limited information is available on how to predict these outcomes.

Objective

We aimed to assess whether adding the initial National Institutes of Health Stroke Scale (NIHSS) score or modified Rankin scale (mRS) score at discharge improved predictive models of 30-day nonelective readmission or 30-day mortality poststroke.

Methods

Using a cohort of patients with ischemic stroke in a large multiethnic integrated health care system from June 15, 2018, to April 29, 2020, we tested 2 predictive models for a composite outcome (30-day nonelective readmission or death). The models were based on administrative data (Length of Stay, Acuity, Charlson Comorbidities, Emergency Department Use score; LACE) as well as a comprehensive model (Transition Support Level; TSL). The models, initial NIHSS score, and mRS scores at discharge, were tested independently and in combination with age and sex. We assessed model performance using the area under the receiver operator characteristic (c-statistic), Nagelkerke pseudo-R2, and Brier score.

Results

The study cohort included 4843 patients with 5014 stroke hospitalizations. Average age was 71.9 (SD 14) years, 50.6% (2537/5014) were female, and 52.1% (2614/5014) were White. Median initial NIHSS score was 4 (IQR 2-8). There were 538 (10.7%) nonelective readmissions and 150 (3.9%) deaths within 30 days. The logistic models revealed that the best performing models were TSL (c-statistic=0.69) and TSL plus mRS score at discharge (c-statistic=0.69).

Conclusions

We found that neither the initial NIHSS score nor the mRS score at discharge significantly enhanced the predictive ability of the LACE or TSL models. Future efforts at prediction of short-term stroke outcomes will need to incorporate new data elements.

Evaluation of an Advanced Care at Home Pilot Program

This quality improvement study evaluates length of stay and health care use outcomes associated with an advanced care at home program.

Prediction of 1 and 2 week nonelective hospitalization and sepsis hospitalization risk in adults

We developed and validated models to predict 1- and 2-week risk of non-elective hospitalization (NEH) and sepsis hospitalization following outpatient clinic, emergency department treat and release (EDTR), or hospitalization encounters. We employed data from 4,488,579 adults with 1,481,430 hospital, 6,035,296 EDTR, and 86,013,893 clinic encounters. Predictors included administrative, clinical (laboratory tests, vital signs), utilization, and prescription pattern data. We employed 2012-2018 data for development and 2019 data for validation. In validation datasets, discrimination (area under the receiver operator characteristic curve) ranged from 0.687 for NEH within 1 week of hospital discharge to 0.904 for sepsis hospitalization within 2 weeks of clinic visits. At a sensitivity of 40%, numbers needed to evaluate (NNE) ranged from 4.3 for NEH within 2 weeks of hospitalization to 45 for sepsis hospitalization within 1 week of a clinic visit. Our models have potentially clinically actionable NNEs and could support clinical programs for the prevention of short-term hospitalizations and sepsis.

Blood donor SARS-CoV-2 infection or vaccination and adverse outcomes in plasma and platelet transfusion recipients

BACKGROUND

Despite data supporting the safety of SARS-CoV-2 vaccination, concerns regarding the receipt of blood products from donors previously infected or vaccinated against SARS-CoV-2 persist. We assessed whether transfusions of plasma or platelet products from donors with prior SARS-CoV-2 infection or vaccination were associated with adverse outcomes in patients without COVID-19.

METHODS

We linked donor SARS-CoV-2 spike and nucleocapsid antibody data and vaccination history to blood products transfused between June 1, 2020 and March 31, 2022. We used logistic regression, adjusting for demographics and comorbidities, to calculate odds ratios and 95% confidence intervals (CI) for posttransfusion thrombosis, increased respiratory requirement, and hospital mortality. Outcomes were assessed as per transfused unit from previously infected or vaccinated donors compared to units from uninfected or unvaccinated donors.

RESULTS

Among 8715 hospitalizations of 7773 transfusion recipients linked to donor SARS-CoV-2 antibody data, there were 251 thromboses, 700 hospitalizations with increased respiratory requirements, and 1443 deaths. Among 15,167 transfused plasma units, 4993 and 1106 were from vaccinated donors and previously infected donors, respectively. Among 19,295 transfused platelet units, 8530 and 1368 were from vaccinated and previously infected donors, respectively. There were no associations between the transfusion of blood products from vaccinated or previously infected donors and thrombosis, increased respiratory requirements, or hospital mortality (all CI including 1). Nor were there associations between the receipt of blood products from recently infected or vaccinated donors or high SARS-CoV-2 antibody titers and adverse outcomes.

DISCUSSION

Donor SARS-Cov-2 infection and vaccination were not associated with adverse patient outcomes and do not need to be considered in blood allocation.

Laboratory Test Use and Values in the Last Year of Life-a Matched Cohort Design

Background

As individuals approach death, they experience declines in their cognitive, physical, motor, sensory, physiologic, and psychosocial functions. In this exploratory study we examined individuals' physiologic changes in the last year of life by examining laboratory tests commonly used in clinical practice.

Methods

Using health administrative datasets, we conducted an observational matched cohort study to assess laboratory test use and values over a decedent's last 12 months and a matched observation window for non-decedents. Laboratory tests included tests for electrolytes: potassium and sodium; complete blood count: hemoglobin and leukocytes; diabetes: hemoglobin A1c; and kidney or liver function: albumin-serum, alanine aminotransferase, and creatinine.

Results

We identified 376,463 decedents, 367,474 (97.6%) of whom were matched to non-decedents (similar age and sex). For each test, the proportion of non-decedents who received the test was stable over the 12-month observation period. A higher proportion of decedents had a laboratory test than non-decedents for all but the diabetes test. As decedents neared death, there was a gradual increase in test use until their final month of life, when test use dramatically increased. Across all laboratory tests, test values remained similar for non-decedents over the 12-month observation period. However, for decedents, there were differences in the magnitude and direction of the test values over the 12 months.

Conclusion

Our findings indicate distinct changes in decedents' laboratory test use and values over their last 12 months. Future work should explore whether laboratory tests could predict survival or improve the performance of mortality prediction models.

Developing an electronic surprise question to predict end-of-life prognosis in a prospective cohort study of acute hospital admissions

OBJECTIVE

Determining the accuracy of a method calculating the Gold Standards Framework Surprise Question (GSFSQ) equivalent end-of-life prognosis amongst hospital inpatients.

DESIGN

A prospective cohort study with regression calculated 1-year mortality probability. Probability cut points triaged unknown prognosis into the GSFSQ equivalent 'Yes' or 'No' survival categories (> or < 1-year respectively), with subsidiary classification of 'No'. Prediction was tested against prospective mortality.

SETTING

An acute NHS hospital.

PARTICIPANTS

18,838 acute medical admissions.

INTERVENTIONS

Allocation of mortality probability by binary logistic regression model (X2=6,650.2, p<0.001, r2 = 0.43) and stepwise algorithmic risk-stratification.

MAIN OUTCOME MEASURE

Prospective mortality at 1-year.

RESULTS

End-of-life prognosis was unknown in 67.9%. The algorithm's prognosis allocation (100% vs baseline 32.1%) yielded cohorts of GSFSQ-Yes 15,264 (81%), GSFSQ-No Green 1,771 (9.4%) and GSFSQ-No Amber or Red 1,803 (9.6%). There were 5,043 (26.8%) deaths at 1-year. In Cox's survival, model allocated cohorts were discrete for mortality (GSFSQ-Yes 16.4% v GSFSQ-No 71.0% (p<0.001). For the GSFSQ-No classification, the mortality odds ratio was 12.4 (11.4-13.5) (p<0.001) vs GSFSQ-Yes (c-statistic 0.72 (0.70-0.73), p<0.001; accuracy, positive and negative predictive values 81.2%, 83.6%, 83.6%, respectively). Had the tool been utilised at the time of admission, the potential to reduce possibly avoidable subsequent hospital admissions, death-in-hospital and bed days was significant (p<0.001).

CONCLUSION

This study is unique in methodology with prospectively evidenced outcomes. The model algorithm allocated GSFSQ equivalent EOL prognosis universally to a cohort of acutely admitted patients with statistical accuracy validated against prospective mortality outcomes.

Pharmacist vs physician management of e-visit requests for COVID-19 medication: A randomized clinical trial

BACKGROUND

Electronic visits (e-visits), defined as structured asynchronous electronic messages between patients and clinicians requiring clinical decision-making, are being increasingly used to enhance access to outpatient health care services, but the primary care physicians who typically manage them face work overflow. Pharmacists have been proposed to manage e-visits that lead to prescription requests, but scant evidence exists about the effectiveness of this approach.

OBJECTIVE

To compare pharmacist management of structured asynchronous e-visit requests for COVID-19 medication with physician management regarding quality of care, timeliness, and patient care experience.

METHODS

This cluster-randomized clinical trial included adults from 17 medical facilities of Kaiser Permanente Northern California who made e-visits requesting COVID-19 medication (nirmatrelvir-ritonavir) from October 9 to December 11, 2023. In the Pharmacist Care group, a regional team of pharmacists managed e-visits for COVID-19 medication; in the Physician Care group, pools of adult and family medicine physicians managed these visits. The primary outcome was whether a patient with 1 or more potential serious drug-drug interactions received counseling via an electronic secure message. Secondary outcomes included prescribing rates, time to the prescription, and patient perceptions of care quality.

RESULTS

Among the 1,753 eligible patients (mean age = 52.2 [SD = 15.9] years; 57.7% female), 642 received Pharmacist Care and 1,111 received Physician Care. The percentage of patients with a potential drug-drug interaction who were sent counseling messages by the clinician did not differ between the Pharmacist Care (76 of 79 [96.2%]) and Physician Care groups (193 of 201 [96.0%]) (risk difference [RD] = 0.18%; 95% CI = -4.8% to 5.2%). The pharmacist and physician groups had similar rates of prescribing (87.4% vs 84.4%; RD = 2.9; 95% CI = -0.4 to 6.3). Pharmacist Care compared with Physician Care had faster mean time from the initial e-visit submission to the resulting prescription (1.0 vs 2.5 hours; RD = -1.5; 95% CI = -1.9 to -1.2). Pharmacist Care took more clinician time per visit than Physician Care (10.7 vs 4.2 minutes), resulting in higher estimated cost ($11.40 vs $6.70). After the study period, the pharmacist team made protocol changes to improve workflow efficiency, and a follow-up analysis 12 months later found significant reductions in per-visit time (to 5.7 minutes) and estimated cost (to $6.03) under Pharmacist Care. Patient perceptions of care did not differ significantly between groups.

CONCLUSIONS

Pharmacist care and physician care for patient e-visits for COVID-19 medication both yielded high quality of care, with no significant group differences. Evaluation of pharmacist care may be warranted for other e-visits designed to facilitate medication prescribing.

CLINICAL TRIAL

ClinicalTrials.gov NCT06096863.

Development and validation of an EHR-based risk prediction model for geriatric patients undergoing urgent and emergency surgery

BACKGROUND

Clinical determination of patients at high risk of poor surgical outcomes is complex and may be supported by clinical tools to summarize the patient's own personalized electronic health record (EHR) history and vitals data through predictive risk models. Since prior models were not readily available for EHR-integration, our objective was to develop and validate a risk stratification tool, named the Assessment of Geriatric Emergency Surgery (AGES) score, predicting risk of 30-day major postoperative complications in geriatric patients under consideration for urgent and emergency surgery using pre-surgical existing electronic health record (EHR) data.

METHODS

Patients 65-years and older undergoing urgent or emergency non-cardiac surgery within 21 hospitals 2017-2021 were used to develop the model (randomly split: 80% training, 20% test). The primary outcome was a 30-day composite outcome including several postoperative major complications and mortality; secondary outcomes included common individual complications included in the primary composite outcome (sepsis, progressive renal insufficiency or renal failure, and mortality). Patients' EHR-based clinical history, vital signs, labs, and demographics were included in logistic regression, LASSO, decision tree, Random Forest, and XGBoost models. Area under the receiver operating characteristics curve (AUCROC) was used to compare model performance.

RESULTS

Overall, 66,262 patients (median [IQR] age 78 [(70.9-84.0], female 53.9%, White race 68.5%) received urgent or emergency non-cardiac surgery (25.7% orthopedic cases, 21.9% general surgery cases). AUCROC ranged from 0.655 (Decision Tree) - 0.804 (XGBoost) for the primary composite outcome. XGBoost AUCROC was 0.823, 0.781, and 0.839 in predicting outcomes of sepsis, progressive renal insufficiency or renal failure, and mortality, respectively.

CONCLUSIONS

We developed a model to accurately predict major postoperative complications in geriatric patients undergoing urgent or emergency surgery using the patient's own existing EHR data. EHR implementation of this model could efficiently support clinicians' surgical risk assessment and perioperative decision-making discussions in this vulnerable patient population.

Duration of aromatase inhibitor use and long-term cardiovascular risk in breast cancer survivors

BACKGROUND

There are limited data on duration of aromatase inhibitor (AI) and cardiovascular disease (CVD) risk in breast cancer (BC) survivors. We examined the risk of CVD and mortality associated with the duration of AI use in postmenopausal women with early stage hormone receptor-positive BC.

METHODS

Postmenopausal women diagnosed with hormone receptor-positive BC (n = 5853) who used an AI were included. Cause-specific hazards models estimated hazard ratios (HRs) and 95% confidence intervals (CIs) for associations between AI use duration (short term: >0 and <2 years; intermediate term: ≥2 and <5 years; long term: ≥5 years) and CVD and mortality outcomes. The landmark method was used to avoid immortal time bias; the selected landmark was 6 years after BC diagnosis.

RESULTS

Anastrozole was the AI predominantly prescribed (95.4%). Over a median follow-up of 3 years for women who survived 6 years after BC diagnosis, a lower risk of stroke was observed in intermediate-term AI users (HR = 0.60, 95% CI = 0.37 to 0.96) and long-term AI users (HR = 0.51, 95% CI = 0.30 to 0.85), than in short-term AI users. The longer duration of AI use was also associated with lower risk of all-cause mortality and non-CVD-related mortality. In addition, long-term AI users were at 37% lower risk of CVD-related mortality than short-term AI users. No statistically significant differences were observed in risks of major adverse cardiovascular events, ischemic heart disease, and heart failure across the 3 groups.

CONCLUSION

Among postmenopausal women with early stage hormone receptor-positive BC who survived 6 years after BC diagnosis, longer duration of AI use was not associated with elevated CVD risk.

The Complete Inpatient Record Using Comprehensive Electronic Data (CIRCE) project: A team-based approach to clinically validated, research-ready electronic health record data

Introduction

The rapid adoption of electronic health record (EHR) systems has resulted in extensive archives of data relevant to clinical research, hospital operations, and the development of learning health systems. However, EHR data are not frequently available, cleaned, standardized, validated, and ready for use by stakeholders. We describe an in-progress effort to overcome these challenges with cooperative, systematic data extraction and validation.

Methods

A multi-disciplinary team of investigators collaborated to create the Complete Inpatient Record Using Comprehensive Electronic Data (CIRCE) Project dataset, which captures EHR data from six hospitals within the University of Pennsylvania Health System. Analysts and clinical researchers jointly iteratively reviewed SQL queries and their output to validate desired data elements. Data from patients aged ≥18 years with at least one encounter at an acute care hospital or hospice occurring since 7/1/2017 were included. The CIRCE Project includes three layers: (1) raw data comprised of direct SQL query output, (2) cleaned data with errors removed, and (3) transformed data with standardized implementations of commonly used case definitions and clinical scores.

Results

Between July 1, 2017 and December 31, 2023, the dataset captured 1 629 920 encounters from 740 035 patients. Most encounters were emergency department only visits (n = 965 834, 59.3%), followed by inpatient admissions without an intensive care unit admission (n = 518 367, 23.7%). The median age was 46.9 years (25th-75th percentiles = 31.1-64.7) at the time of the first encounter. Most patients were female (n = 418 303, 56.5%), a significant proportion were of non-White race (n = 272 018, 36.8%), and 54 625 (7.4%) were of Hispanic/Latino ethnicity.

Conclusions

The CIRCE Project represents a novel cooperative research model to capture clinically validated EHR data from a large diverse academic health system in the greater Philadelphia region and is designed to facilitate collaboration and data sharing to support learning health system activities. Ultimately, these data will be de-identified and converted to a publicly available resource.

Machine Learning for Targeted Advance Care Planning in Cancer Patients: A Quality Improvement Study

CONTEXT

Prognostication challenges contribute to delays in advance care planning (ACP) for patients with cancer near the end of life (EOL).

OBJECTIVES

Examine a quality improvement mortality prediction algorithm intervention's impact on ACP documentation and EOL care.

METHODS

We implemented a validated mortality risk prediction machine learning model for solid malignancy patients admitted from the emergency department (ED) to a dedicated solid malignancy unit at Duke University Hospital. Clinicians received an email when a patient was identified as high-risk. We compared ACP documentation and EOL care outcomes before and after the notification intervention. We excluded patients with intensive care unit (ICU) admission in the first 24 hours. Comparisons involved chi-square/Fisher's exact tests and Wilcoxon rank sum tests; comparisons stratified by physician specialty employ Cochran-Mantel-Haenszel tests.

RESULTS

Preintervention and postintervention cohorts comprised 88 and 77 patients, respectively. Most were White, non-Hispanic/Latino, and married. ACP conversations were documented for 2.3% of hospitalizations preintervention vs. 80.5% postintervention (P<0.001), and if the attending physician notified was a palliative care specialist (4.1% vs. 84.6%) or oncologist (0% vs. 76.3%) (P<0.001). There were no differences between groups in length of stay (LOS), hospice referral, code status change, ICU admissions or LOS, 30-day readmissions, 30-day ED visits, and inpatient and 30-day deaths.

CONCLUSION

Identifying patients with cancer and high mortality risk via machine learning elicited a substantial increase in documented ACP conversations but did not impact EOL care. Our intervention showed promise in changing clinician behavior. Further integration of this model in clinical practice is ongoing.

Development and Validation of the Hospital Medicine Safety Sepsis Initiative Mortality Model

BACKGROUND

When comparing outcomes after sepsis, it is essential to account for patient case mix to make fair comparisons. We developed a model to assess risk-adjusted 30-day mortality in the Michigan Hospital Medicine Safety sepsis initiative (HMS-Sepsis).

RESEARCH QUESTION

Can HMS-Sepsis registry data adequately predict risk of 30-day mortality? Do performance assessments using adjusted vs unadjusted data differ?

STUDY DESIGN AND METHODS

Retrospective cohort of community-onset sepsis hospitalizations in the HMS-Sepsis registry (April 2022-September 2023), with split derivation (70%) and validation (30%) cohorts. We fit a risk-adjustment model (HMS-Sepsis mortality model) incorporating acute physiologic, demographic, and baseline health data and assessed model performance using concordance (C) statistics, Brier scores, and comparisons of predicted vs observed mortality by deciles of risk. We compared hospital performance (first quintile, middle quintiles, fifth quintile) using observed vs adjusted mortality to understand the extent to which risk adjustment impacted hospital performance assessment.

RESULTS

Among 17,514 hospitalizations from 66 hospitals during the study period, 12,260 hospitalizations (70%) were used for model derivation and 5,254 hospitalizations (30%) were used for model validation. Thirty-day mortality for the total cohort was 19.4%. The final model included 13 physiologic variables, two physiologic interactions, and 16 demographic and chronic health variables. The most significant variables were age, metastatic solid tumor, temperature, altered mental status, and platelet count. The model C statistic was 0.82 for the derivation cohort, 0.81 for the validation cohort, and ≥ 0.78 for all subgroups assessed. Overall calibration error was 0.0%, and mean calibration error across deciles of risk was 1.5%. Standardized mortality ratios yielded different assessments than observed mortality for 33.9% of hospitals.

INTERPRETATION

The HMS-Sepsis mortality model showed strong discrimination and adequate calibration and reclassified one-third of hospitals to a different performance category from unadjusted mortality. Based on its strong performance, the HMS-Sepsis mortality model may aid in fair hospital benchmarking, assessment of temporal changes, and observational causal inference analysis.

Adherence to cardiovascular medications and risk of cardiovascular disease in breast cancer patients: A causal inference approach in the Pathways Heart Study

PURPOSE

Women with breast cancer (BC) are at high risk of developing cardiovascular disease (CVD). We examined adherence to CVD medications and their association with major CVD events over 14 years of follow-up in the Pathways Heart Study, a prospective study of 4,776 stage I-III BC patients diagnosed from 2005-2013.

METHODS

Eligibility included being alive 6 months post-BC diagnosis, with dyslipidemia, hypertension, or diabetes at diagnosis along with ≥1 prior outpatient order or dispensing for a statin, anti-hypertensive, or diabetes medication, respectively, in the 30 months prior. Medication adherence was measured from pharmacy data to calculate cumulative average adherence (CAA). Incident heart failure (HF), ischemic heart disease (IHD), and stroke were determined via validated diagnosis and procedure codes. Working marginal structural models (MSM) fitted with inverse probability weighting evaluated the effect of adherence regimens on the hazards for each CVD event, while controlling for baseline and time-varying confounders. MSM parameterizations included: 1) CAA<100% versus CAA = 100% (ref), 2) CAA<80% versus CAA≥80% (ref) and 3) CAA<80% versus 80%≤CAA<100% versus CAA = 100%.

RESULTS

Poor statin adherence (CAA<80%) was associated with higher risk of composite CVD (HR = 2.54; 95% CI: 1.09, 5.94) versus CAA≥80%. Poor statin adherence was also associated with a higher risk of stroke (HR = 8.13; 95% CI: 2.03, 32.51) but not risk of IHD and HF. Further, compared with perfect adherence (CAA = 100%), good adherence (80%≤CAA<100%) was associated with lower risk (HR = 0.35; 95% CI: 0.13, 0.92) while poor adherence (CAA<80%) was associated with higher risk of composite CVD (HR = 2.45; 95% CI: 1.05, 5.70). Levels of adherence to anti-hypertensives and diabetes medications had mixed or null associations with risk of CVD.

CONCLUSIONS

Maintaining good adherence (≥80%) to statins after BC treatment is beneficial for cardiovascular health in patients with dyslipidemia. Future studies should determine factors associated with lower adherence to statins and ways to improve adherence.

Socioeconomic factors associated with uptake and satisfaction with a post-hospitalization meals benefit in Medicare Advantage

BACKGROUND

Kaiser Permanente Southern California began offering a 4-week supplemental benefit of home-delivered meals to Medicare Advantage members after discharge from a hospitalization for heart failure and other medical conditions in 2021. The purpose of this study is to explore the associations between socioeconomic disadvantage and food insecurity with patient uptake of and satisfaction with the meals.

METHODS

Data for this cross-sectional study were drawn from survey and electronic medical record data for members referred for the meals benefit (n = 6169) and linked to a hospitalization encounter (n = 2254) between January and December 2021. Uptake was assessed using vendor records; measures of socioeconomic status included the neighborhood deprivation index (NDI) and prior receipt of medical financial assistance (MFA) from the health system. Patients were invited to complete an email or phone survey about their satisfaction with the meals and food insecurity. Multivariable log-binomial regression models were used to examine the association between socioeconomic disadvantage and food insecurity with meals uptake and satisfaction.

RESULTS

Sixty-two percent of patients referred for the benefit accepted the meals (mean age: 79 ± 9, 59% people of color). While there was no significant relationship between NDI and meals uptake (RR: 0.99, 95% CI: 0.92-1.07, p = 0.77), patients who received prior MFA were more likely to accept the meals (RR: 1.09, 95% CI: 1.02-1.16, p < 0.01). Sixty-nine percent of patients who completed the survey (23% response rate) reported that meals were very or extremely helpful. Patients with food insecurity (29% of survey respondents) were more likely to report that the meals were helpful for their recovery compared to food secure patients (RR: 1.21, 95% CI: 1.09-1.35, p < 0.01).

CONCLUSIONS

The home-delivered meals appeared to be particularly utilized by and helpful to patients with greater financial strain and/or food insecurity, suggesting that supplemental benefits could be more targeted toward addressing unmet needs of vulnerable adults.

A simple electronic medical record-based predictors of illness severity in sepsis (sepsis) score

OBJECTIVE

Current scores for predicting sepsis outcomes are limited by generalizability, complexity, and electronic medical record (EMR) integration. Here, we validate a simple EMR-based score for sepsis outcomes in a large multi-centre cohort.

DESIGN

A simple electronic medical record-based predictor of illness severity in sepsis (SEPSIS) score was developed (4 additive lab-based predictors) using a population-based retrospective cohort study.

SETTING

Internal medicine services across four academic teaching hospitals in Toronto, Canada from April 2010-March 2015 (primary cohort) and 2015-2019 (secondary cohort).

PATIENTS

We identified patients admitted with sepsis based upon receipt of antibiotics and positive cultures.

MEASUREMENTS AND MAIN RESULTS

The primary outcome was in-hospital mortality and secondary outcomes were ICU admission at 72 hours, and hospital length of stay (LOS). We calculated the area under the receiver operating curve (AUROC) for the SEPSIS score, qSOFA, and NEWS2. We then evaluated the SEPSIS score in a secondary cohort (2015-2019) of hospitalized patients receiving antibiotics. Our primary cohort included 1,890 patients with a median age of 72 years (IQR: 56-83). 9% died during hospitalization, 18.6% were admitted to ICU, and mean LOS was 12.7 days (SD: 21.5). In the primary and secondary (2015-2019, 4811 patients) cohorts, the AUROCs of the SEPSIS score for predicting in-hospital mortality were 0.63 and 0.64 respectively, which were similar to NEWS2 (0.62 and 0.67) and qSOFA (0.62 and 0.68). AUROCs for predicting ICU admission at 72 hours, and length of stay > 14 days, were similar between scores, in the primary and secondary cohorts. All scores had comparable calibration for predicting mortality.

CONCLUSIONS

An EMR-based SEPSIS score shows a similar ability to predict important clinical outcomes compared with other validated scores (qSOFA and NEWS2). Because of the SEPSIS score's simplicity, it may prove a useful tool for clinical and research applications.

Predicting the risk of 1-year mortality among patients hospitalized for acute heart failure in China

BACKGROUND

We aimed to develop and validate a model to predict 1-year mortality risk among patients hospitalized for acute heart failure (AHF), build a risk score and interpret its application in clinical decision making.

METHODS

By using data from China Patient-Centred Evaluative Assessment of Cardiac Events Prospective Heart Failure Study, which prospectively enrolled patients hospitalized for AHF in 52 hospitals across 20 provinces, we used multivariate Cox proportional hazard model to develop and validate a model to predict 1-year mortality.

RESULTS

There were 4,875 patients included in the study, 857 (17.58%) of them died within 1-year following discharge of index hospitalization. A total of 13 predictors were selected to establish the prediction model, including age, medical history of chronic obstructive pulmonary disease and hypertension, systolic blood pressure, Kansas City Cardiomyopathy Questionnaire-12 score, angiotensin converting enzyme inhibitor or angiotensin receptor blocker at discharge, discharge symptom, N-terminal pro-brain natriuretic peptide, high-sensitivity troponin T, serum creatine, albumin, blood urea nitrogen, and highly sensitive C-reactive protein. The model showed a high performance on discrimination (C-index was 0.759 [95% confidence interval: 0.739, 0.778] in development cohort and 0.761 [95% confidence interval: 0.731, 0.791] in validation cohort), accuracy, calibration, and outperformed than several existed risk scores. A point-based risk score was built to stratify low- (0-12), intermediate- (13-16), and high-risk group (≥17) among patients.

CONCLUSIONS

A prediction model using readily available predictors was developed and internal validated to predict 1-year mortality risk among patients hospitalized for AHF. It may serve as a useful tool for individual risk stratification and informing decision making to improve clinical care.

Characterizing medical patients with delirium: A cohort study comparing ICD-10 codes and a validated chart review method

BACKGROUND

Delirium is a major cause of preventable mortality and morbidity in hospitalized adults, but accurately determining rates of delirium remains a challenge.

OBJECTIVE

To characterize and compare medical inpatients identified as having delirium using two common methods, administrative data and retrospective chart review.

METHODS

We conducted a retrospective study of 3881 randomly selected internal medicine hospital admissions from six acute care hospitals in Toronto and Mississauga, Ontario, Canada. Delirium status was determined using ICD-10-CA codes from hospital administrative data and through a previously validated chart review method. Baseline sociodemographic and clinical characteristics, processes of care and outcomes were compared across those without delirium in hospital and those with delirium as determined by administrative data and chart review.

RESULTS

Delirium was identified in 6.3% of admissions by ICD-10-CA codes compared to 25.7% by chart review. Using chart review as the reference standard, ICD-10-CA codes for delirium had sensitivity 24.1% (95%CI: 21.5-26.8%), specificity 99.8% (95%CI: 99.5-99.9%), positive predictive value 97.6% (95%CI: 94.6-98.9%), and negative predictive value 79.2% (95%CI: 78.6-79.7%). Age over 80, male gender, and Charlson comorbidity index greater than 2 were associated with misclassification of delirium. Inpatient mortality and median costs of care were greater in patients determined to have delirium by ICD-10-CA codes (5.8% greater mortality, 95% CI: 2.0-9.5 and $6824 greater cost, 95%CI: 4713-9264) and by chart review (11.9% greater mortality, 95%CI: 9.5-14.2% and $4967 greater cost, 95%CI: 4415-5701), compared to patients without delirium.

CONCLUSIONS

Administrative data are specific but highly insensitive, missing most cases of delirium in hospital. Mortality and costs of care were greater for both the delirium cases that were detected and missed by administrative data. Better methods of routinely measuring delirium in hospital are needed.

Video-Assisted vs Robotic-Assisted Lung Lobectomies for Operating Room Resource Utilization and Patient Outcomes

Importance

With increased use of robots, there is an inadequate understanding of minimally invasive modalities' time costs. This study evaluates the operative durations of robotic-assisted vs video-assisted lung lobectomies.

Objective

To compare resource utilization, specifically operative time, between video-assisted and robotic-assisted thoracoscopic lung lobectomies.

Design, Setting, and Participants

This retrospective cohort study evaluated patients aged 18 to 90 years who underwent minimally invasive (robotic-assisted or video-assisted) lung lobectomy from January 1, 2020, to December 31, 2022, with 90 days' follow-up after surgery. The study included multicenter electronic health record data from 21 hospitals within an integrated health care system in Northern California. Thoracic surgery was regionalized to 4 centers with 14 board-certified general thoracic surgeons.

Exposures

Robotic-assisted or video-assisted lung lobectomy.

Main Outcomes and Measures

The primary outcome was operative duration (cut to close) in minutes. Secondary outcomes were length of stay, 30-day readmission, and 90-day mortality. Comparisons between video-assisted and robotic-assisted lobectomies were generated using the Wilcoxon rank sum test for continuous variables and the χ2 test for categorical variables. The average treatment effects were estimated with augmented inverse probability treatment weighting (AIPTW). Patient and surgeon covariates were adjusted for and included patient demographics, comorbidities, and case complexity (age, sex, race and ethnicity, neighborhood deprivation index, body mass index, Charlson Comorbidity Index score, nonelective hospitalizations, emergency department visits, a validated laboratory derangement score, a validated institutional comorbidity score, a surgeon-designated complexity indicator, and a procedural code count), and a primary surgeon-specific indicator.

Results

The study included 1088 patients (median age, 70.1 years [IQR, 63.3-75.8 years]; 704 [64.7%] female), of whom 446 (41.0%) underwent robotic-assisted and 642 (59.0%) underwent video-assisted lobectomy. The median unadjusted operative duration was 172.0 minutes (IQR, 128.0-226.0 minutes). After AIPTW, there was less than a 10% difference in all covariates between groups, and operative duration was a median 20.6 minutes (95% CI, 12.9-28.2 minutes; P < .001) longer for robotic-assisted compared with video-assisted lobectomies. There was no difference in adjusted secondary patient outcomes, specifically for length of stay (0.3 days; 95% CI, -0.3 to 0.8 days; P = .11) or risk of 30-day readmission (adjusted odds ratio, 1.29; 95% CI, 0.84-1.98; P = .13). The unadjusted 90-day mortality rate (1.3% [n = 14]) was too low for the AIPTW modeling process.

Conclusions and Relevance

In this cohort study, there was no difference in patient outcomes between modalities, but operative duration was longer in robotic-assisted compared with video-assisted lung lobectomy. Given that this elevated operative duration is additive when applied systematically, increased consideration of appropriate patient selection for robotic-assisted lung lobectomy is needed to improve resource utilization.

Evaluation of an outreach programme for patients with COVID-19 in an integrated healthcare delivery system: a retrospective cohort study

OBJECTIVES

In the first year of the COVID-19 pandemic, health systems implemented programmes to manage outpatients with COVID-19. The goal was to expedite patients' referral to acute care and prevent overcrowding of medical centres. We sought to evaluate the impact of such a programme, the COVID-19 Home Care Team (CHCT) programme.

DESIGN

Retrospective cohort.

SETTING

Kaiser Permanente Northern California.

PARTICIPANTS

Adult members before COVID-19 vaccine availability (1 February 2020-31 January 2021) with positive SARS-CoV-2 tests.

INTERVENTION

Virtual programme to track and treat patients with 'CHCT programme'.

OUTCOMES

The outcomes were (1) COVID-19-related emergency department visit, (2) COVID-19-related hospitalisation and (3) inpatient mortality or 30-day hospice referral.

MEASURES

We estimated the average effect comparing patients who were and were not treated by CHCT. We estimated propensity scores using an ensemble super learner (random forest, XGBoost, generalised additive model and multivariate adaptive regression splines) and augmented inverse probability weighting.

RESULTS

There were 98 585 patients with COVID-19. The majority were followed by CHCT (n=80 067, 81.2%). Patients followed by CHCT were older (mean age 43.9 vs 41.6 years, p<0.001) and more comorbid with COmorbidity Point Score, V.2, score ≥65 (1.7% vs 1.1%, p<0.001). Unadjusted analyses showed more COVID-19-related emergency department visits (9.5% vs 8.5%, p<0.001) and hospitalisations (3.9% vs 3.2%, p<0.001) in patients followed by CHCT but lower inpatient death or 30-day hospice referral (0.3% vs 0.5%, p<0.001). After weighting, there were higher rates of COVID-19-related emergency department visits (estimated intervention effect -0.8%, 95% CI -1.4% to -0.3%) and hospitalisation (-0.5%, 95% CI -0.9% to -0.1%) but lower inpatient mortality or 30-day hospice referral (-0.5%, 95% CI -0.7% to -0.3%) in patients followed by CHCT.

CONCLUSIONS

Despite CHCT following older patients with higher comorbidity burden, there appeared to be a protective effect. Patients followed by CHCT were more likely to present to acute care and less likely to die inpatient.

Clinical outcomes in hospitalized plasma and platelet transfusion recipients prior to and following widespread blood donor SARS-CoV-2 infection and vaccination

BACKGROUND

The safety of transfusion of SARS-CoV-2 antibodies in high plasma volume blood components to recipients without COVID-19 is not established. We assessed whether transfusion of plasma or platelet products during periods of increasing prevalence of blood donor SARS-CoV-2 infection and vaccination was associated with changes in outcomes in hospitalized patients without COVID-19.

METHODS

We conducted a retrospective cohort study of hospitalized adults who received plasma or platelet transfusions at 21 hospitals during pre-COVID-19 (3/1/2018-2/29/2020), COVID-19 pre-vaccine (3/1/2020-2/28/2021), and COVID-19 post-vaccine (3/1/2021-8/31/2022) study periods. We used multivariable logistic regression with generalized estimating equations to adjust for demographics and comorbidities to calculate odds ratios (ORs) and 95% confidence intervals (CIs).

RESULTS

Among 21,750 hospitalizations of 18,584 transfusion recipients without COVID-19, there were 697 post-transfusion thrombotic events, and oxygen requirements were increased in 1751 hospitalizations. Intensive care unit length of stay (n = 11,683) was 3 days (interquartile range 1-5), hospital mortality occurred in 3223 (14.8%), and 30-day rehospitalization in 4144 (23.7%). Comparing the pre-COVID, pre-vaccine and post-vaccine study periods, there were no trends in thromboses (OR 0.9 [95% CI 0.8, 1.1]; p = .22) or oxygen requirements (OR 1.0 [95% CI 0.9, 1.1]; p = .41). In parallel, there were no trends across study periods for ICU length of stay (p = .83), adjusted hospital mortality (OR 1.0 [95% CI 0.9-1.0]; p = .36), or 30-day rehospitalization (p = .29).

DISCUSSION

Transfusion of plasma and platelet blood components collected during the pre-vaccine and post-vaccine periods of the COVID-19 pandemic was not associated with increased adverse outcomes in transfusion recipients without COVID-19.

An Update to the Kaiser Permanente Inpatient Risk Adjustment Methodology Accurately Predicts In-Hospital Mortality: a Retrospective Cohort Study

BACKGROUND

Methods to accurately predict the risk of in-hospital mortality are important for applications including quality assessment of healthcare institutions and research.

OBJECTIVE

To update and validate the Kaiser Permanente inpatient risk adjustment methodology (KP method) to predict in-hospital mortality, using open-source tools to measure comorbidity and diagnosis groups, and removing troponin which is difficult to standardize across modern clinical assays.

DESIGN

Retrospective cohort study using electronic health record data from GEMINI. GEMINI is a research collaborative that collects administrative and clinical data from hospital information systems.

PARTICIPANTS

Adult general medicine inpatients at 28 hospitals in Ontario, Canada, between April 2010 and December 2022.

MAIN MEASURES

The outcome was in-hospital mortality, modeled by diagnosis group using 56 logistic regressions. We compared models with and without troponin as an input to the laboratory-based acute physiology score. We fit and validated the updated method using internal-external cross-validation at 28 hospitals from April 2015 to December 2022.

KEY RESULTS

In 938,103 hospitalizations with 7.2% in-hospital mortality, the updated KP method accurately predicted the risk of mortality. The c-statistic at the median hospital was 0.866 (see Fig. 3) (25th-75th 0.848-0.876, range 0.816-0.927) and calibration was strong for nearly all patients at all hospitals. The 95th percentile absolute difference between predicted and observed probabilities was 0.038 at the median hospital (25th-75th 0.024-0.057, range 0.006-0.118). Model performance was very similar with and without troponin in a subset of 7 hospitals, and performance was similar with and without troponin for patients hospitalized for heart failure and acute myocardial infarction.

CONCLUSIONS

An update to the KP method accurately predicted in-hospital mortality for general medicine inpatients in 28 hospitals in Ontario, Canada. This updated method can be implemented in a wider range of settings using common open-source tools.

A Data-Driven Analysis of Ward Capacity Strain Metrics That Predict Clinical Outcomes Among Survivors of Acute Respiratory Failure

Supply-demand mismatch of ward resources ("ward capacity strain") alters care and outcomes. Narrow strain definitions and heterogeneous populations limit strain literature. Evaluate the predictive utility of a large set of candidate strain variables for in-hospital mortality and discharge destination among acute respiratory failure (ARF) survivors. In a retrospective cohort of ARF survivors transferred from intensive care units (ICUs) to wards in five hospitals from 4/2017-12/2019, we applied 11 machine learning (ML) models to identify ward strain measures during the first 24 hours after transfer most predictive of outcomes. Measures spanned patient volume (census, admissions, discharges), staff workload (medications administered, off-ward transports, transfusions, isolation precautions, patients per respiratory therapist and nurse), and average patient acuity (Laboratory Acute Physiology Score version 2, ICU transfers) domains. The cohort included 5,052 visits in 43 wards. Median age was 65 years (IQR 56-73); 2,865 (57%) were male; and 2,865 (57%) were white. 770 (15%) patients died in the hospital or had hospice discharges, and 2,628 (61%) were discharged home and 964 (23%) to skilled nursing facilities (SNFs). Ward admissions, isolation precautions, and hospital admissions most consistently predicted in-hospital mortality across ML models. Patients per nurse most consistently predicted discharge to home and SNF, and medications administered predicted SNF discharge. In this hypothesis-generating analysis of candidate ward strain variables' prediction of outcomes among ARF survivors, several variables emerged as consistently predictive of key outcomes across ML models. These findings suggest targets for future inferential studies to elucidate mechanisms of ward strain's adverse effects.

Association of a Medicare Advantage Posthospitalization Home Meal Delivery Benefit With Rehospitalization and Death

Importance

The 2018 Chronic Care Act allowed Medicare Advantage plans to have greater flexibility in offering supplemental benefits, such as meals and services, to address unmet needs of beneficiaries with certain chronic conditions. Based on earlier studies of community-based nutritional support, such programs may result in reduced use.

Objective

To evaluate the association of a 4-week posthospitalization home-delivered meals benefit with 30-day all-cause rehospitalization and mortality in patients admitted for heart failure (HF) and other acute medical conditions (non-HF).

Design, Setting, and Participants

In this cohort study, patients who received meals (the meals group) were compared with 2 controls: (1) no meals in the 2019 historical cohort who would have been eligible for the benefit (the no meals-2019 group) and (2) no meals in the 2021 and 2022 concurrent cohort who were referred but did not receive the meals due to unsuccessful contacts and active declines (the no meals-2021/2022 group). This study took place in a large integrated health care system in southern California among Medicare Advantage members with a hospitalization for HF or other acute medical conditions at 15 Kaiser Permanente hospitals discharged to home.

Exposure

The exposure was receipt of at least 1 and up to 4 shipments of home-delivered meals (total of 56 to 84 meals) after hospital discharge.

Main Outcomes and Measures

The main outcomes were 30-day all-cause composite rehospitalization and death.

Results

A total of 4032 adults with admission to the hospital for HF (mean [SD] age, 79 [9] years; 1951 [48%] White; 2001 [50%] female) and 7944 with non-HF admissions (mean [SD] age, 78 [8] years; 3890 [49%] White; 4149 [52%] female) were included in the analyses. Unadjusted rates of 30-day death and rehospitalization for the meals, no meals-2019, and no meals-2021/2022 cohorts were as follows: HF: 23.3%, 30.1%, and 38.5%; non-HF: 16.5%, 22.4%, and 32.9%, respectively. For HF, exposure to meals was significantly associated with lower odds of 30-day death and rehospitalization compared with the no meals-2021/2022 cohort (OR, 0.55; 95% CI, 0.43-0.71; P < .001) but was not significant compared with the no meals-2019 cohort (OR, 0.86; 95% CI, 0.72-1.04; P = .12). For non-HF, exposure to meals was associated with significantly lower odds of 30-day death and rehospitalization when compared with the no meals-2019 (OR, 0.64; 95% CI, 0.52-0.79; P < .001) and the no meals-2021/2022 (OR, 0.48; 95% CI, 0.37-0.62; P < .001) cohorts.

Conclusions and Relevance

In this cohort study, exposure to posthospitalization home-delivered meals was associated with lower 30-day rehospitalization and mortality; randomized clinical trials are needed to confirm these findings.

The impact of timing of initiating invasive mechanical ventilation in COVID-19-related respiratory failure

PURPOSE

Optimal timing of initiating invasive mechanical ventilation (IMV) in coronavirus disease 2019 (COVID-19)-related respiratory failure is unclear. We hypothesized that a strategy of IMV as opposed to continuing high flow oxygen or non-invasive mechanical ventilation each day after reaching a high FiO2 threshold would be associated with worse in-hospital mortality.

METHODS

Using data from Kaiser Permanente Northern/Southern California's 36 medical centers, we identified patients with COVID-19-related acute respiratory failure who reached ≥80% FiO2 on high flow nasal cannula or non-invasive ventilation. Exposure was IMV initiation each day after reaching high FiO2 threshold (T₀). We developed propensity scores with overlap weighting for receipt of IMV each day adjusting for confounders. We reported relative risk of inpatient death with 95% Confidence Interval.

RESULTS

Of 28,035 hospitalizations representing 21,175 patient-days, 5758 patients were included (2793 received and 2965 did not receive IMV). Patients receiving IMV had higher unadjusted mortality (63.6% versus 18.2%, P < 0.0001). On each day after reaching T₀ through day >10, the adjusted relative risk was higher for those receiving IMV compared to those not receiving IMV (Relative Risk>1).

CONCLUSIONS

Initiation of IMV on each day after patients reach high FiO2 threshold was associated with higher inpatient mortality after adjusting for time-varying confounders. Remaining on high flow nasal cannula or non-invasive ventilation does not appear to be harmful compared to IMV. Prospective evaluation is needed.

Racial Disparities in Length of Stay Among Severely Ill Patients Presenting With Sepsis and Acute Respiratory Failure

Importance

Although racial and ethnic minority patients with sepsis and acute respiratory failure (ARF) experience worse outcomes, how patient presentation characteristics, processes of care, and hospital resource delivery are associated with outcomes is not well understood.

Objective

To measure disparities in hospital length of stay (LOS) among patients at high risk of adverse outcomes who present with sepsis and/or ARF and do not immediately require life support and to quantify associations with patient- and hospital-level factors.

Design, Setting, and Participants

This matched retrospective cohort study used electronic health record data from 27 acute care teaching and community hospitals across the Philadelphia metropolitan and northern California areas between January 1, 2013, and December 31, 2018. Matching analyses were performed between June 1 and July 31, 2022. The study included 102 362 adult patients who met clinical criteria for sepsis (n = 84 685) or ARF (n = 42 008) with a high risk of death at the time of presentation to the emergency department but without an immediate requirement for invasive life support.

Exposures

Racial or ethnic minority self-identification.

Main Outcomes and Measures

Hospital LOS, defined as the time from hospital admission to the time of discharge or inpatient death. Matches were stratified by racial and ethnic minority patient identity, comparing Asian and Pacific Islander patients, Black patients, Hispanic patients, and multiracial patients with White patients in stratified analyses.

Results

Among 102 362 patients, the median (IQR) age was 76 (65-85) years; 51.5% were male. A total of 10.2% of patients self-identified as Asian American or Pacific Islander, 13.7% as Black, 9.7% as Hispanic, 60.7% as White, and 5.7% as multiracial. After matching racial and ethnic minority patients to White patients on clinical presentation characteristics, hospital capacity strain, initial intensive care unit admission, and the occurrence of inpatient death, Black patients experienced longer LOS relative to White patients in fully adjusted matches (sepsis: 1.26 [95% CI, 0.68-1.84] days; ARF: 0.97 [95% CI, 0.05-1.89] days). Length of stay was shorter among Asian American and Pacific Islander patients with ARF (-0.61 [95% CI, -0.88 to -0.34] days) and Hispanic patients with sepsis (-0.22 [95% CI, -0.39 to -0.05] days) or ARF (-0.47 [-0.73 to -0.20] days).

Conclusions and Relevance

In this cohort study, Black patients with severe illness who presented with sepsis and/or ARF experienced longer LOS than White patients. Hispanic patients with sepsis and Asian American and Pacific Islander and Hispanic patients with ARF both experienced shorter LOS. Because matched differences were independent of commonly implicated clinical presentation-related factors associated with disparities, identification of additional mechanisms that underlie these disparities is warranted.

Among-Hospital Variation in Intensive Care Unit Admission Practices and Associated Outcomes for Patients with Acute Respiratory Failure

Rationale: We have previously shown that hospital strain is associated with intensive care unit (ICU) admission and that ICU admission, compared with ward admission, may benefit certain patients with acute respiratory failure (ARF). Objectives: To understand how strain-process-outcomes relationships in patients with ARF may vary among hospitals and what hospital practice differences may account for such variation. Methods: We examined high-acuity patients with ARF who did not require mechanical ventilation or vasopressors in the emergency department (ED) and were admitted to 27 U.S. hospitals from 2013 to 2018. Stratifying by hospital, we compared hospital strain-ICU admission relationships and hospital length of stay (LOS) and mortality among patients initially admitted to the ICU versus the ward using hospital strain as a previously validated instrumental variable. We also surveyed hospital practices and, in exploratory analyses, evaluated their associations with the above processes and outcomes. Results: There was significant among-hospital variation in ICU admission rates, in hospital strain-ICU admission relationships, and in the association of ICU admission with hospital LOS and hospital mortality. Overall, ED patients with ARF (n = 45,339) experienced a 0.82-day shorter median hospital LOS if admitted initially to the ICU compared with the ward, but among the 27 hospitals (n = 224-3,324), this effect varied from 5.85 days shorter (95% confidence interval [CI], -8.84 to -2.86; P < 0.001) to 4.38 days longer (95% CI, 1.86-6.90; P = 0.001). Corresponding ranges for in-hospital mortality with ICU compared with ward admission revealed odds ratios from 0.08 (95% CI, 0.01-0.56; P < 0.007) to 8.89 (95% CI, 1.60-79.85; P = 0.016) among patients with ARF (pooled odds ratio, 0.75). In exploratory analyses, only a small number of measured hospital practices-the presence of a sepsis ED disposition guideline and maximum ED patient capacity-were potentially associated with hospital strain-ICU admission relationships. Conclusions: Hospitals vary considerably in ICU admission rates, the sensitivity of those rates to hospital capacity strain, and the benefits of ICU admission for patients with ARF not requiring life support therapies in the ED. Future work is needed to more fully identify hospital-level factors contributing to these relationships.

Practice Patterns and Outcomes Associated With Anticoagulation Use Following Sepsis Hospitalizations With New-Onset Atrial Fibrillation

BACKGROUND

Practice patterns and outcomes associated with the use of oral anticoagulation for arterial thromboembolism prevention following a hospitalization with new-onset atrial fibrillation (AF) during sepsis are unclear.

METHODS

Retrospective, observational cohort study of patients ≥40 years of age discharged alive following hospitalization with new-onset AF during sepsis across 21 hospitals in the Kaiser Permanente Northern California health care delivery system, years 2011 to 2018. Primary outcomes were ischemic stroke/transient ischemic attack (TIA), with a safety outcome of major bleeding events, both within 1 year of discharge alive from sepsis hospitalization. Adjusted risk differences for outcomes between patients who did and did not receive oral anticoagulation within 30 days of discharge were estimated using marginal structural models fitted by inverse probability weighting using Super Learning within a target trial emulation framework.

RESULTS

Among 82 748 patients hospitalized with sepsis, 3992 (4.8%) had new-onset AF and survived to hospital discharge; mean age was 78±11 years, 53% were men, and 70% were White. Patients with new-onset AF during sepsis averaged 45±33% of telemetry monitoring entries with AF, and 27% had AF present on the day of hospital discharge. Within 1 year of hospital discharge, 89 (2.2%) patients experienced stroke/TIA, 225 (5.6%) had major bleeding, and 1011 (25%) died. Within 30 days of discharge, 807 (20%) patients filled oral anticoagulation prescriptions, which were associated with higher 1-year adjusted risks of ischemic stroke/TIA (5.69% versus 2.32%; risk difference, 3.37% [95% CI, 0.36-6.38]) and no significant difference in 1-year adjusted risks of major bleeding (6.51% versus 7.10%; risk difference, -0.59% [95% CI, -3.09 to 1.91]). Sensitivity analysis of ischemic stroke-only outcomes showed a risk difference of 0.15% (95% CI, -1.72 to 2.03).

CONCLUSIONS

After hospitalization with new-onset AF during sepsis, oral anticoagulation use was uncommon and associated with potentially higher stroke/TIA risk. Further research to inform mechanisms of stroke and TIA and management of new-onset AF after sepsis is needed.

Hospital Strain and Variation in Sepsis ICU Admission Practices and Associated Outcomes

To understand how strain-process-outcome relationships in patients with sepsis may vary among hospitals.

DESIGN

Retrospective cohort study using a validated hospital capacity strain index as a within-hospital instrumental variable governing ICU versus ward admission, stratified by hospital.

SETTING

Twenty-seven U.S. hospitals from 2013 to 2018.

PATIENTS

High-acuity emergency department patients with sepsis who do not require life support therapies.

INTERVENTIONS

None.

MEASUREMENTS AND MAIN RESULTS

The mean predicted probability of ICU admission across strain deciles ranged from 4.9% (lowest ICU-utilizing hospital for sepsis without life support) to 61.2% (highest ICU-utilizing hospital for sepsis without life support). The difference in the predicted probabilities of ICU admission between the lowest and highest strain deciles ranged from 9.0% (least strain-sensitive hospital) to 45.2% (most strain-sensitive hospital). In pooled analyses, emergency department patients with sepsis (n = 90,150) experienced a 1.3-day longer median hospital length of stay (LOS) if admitted initially to the ICU compared with the ward, but across the 27 study hospitals (n = 517-6,564), this effect varied from 9.0 days shorter (95% CI, -10.8 to -7.2; p < 0.001) to 19.0 days longer (95% CI, 16.7-21.3; p < 0.001). Corresponding ranges for inhospital mortality with ICU compared with ward admission revealed odds ratios (ORs) from 0.16 (95% CI, 0.03-0.99; p = 0.04) to 4.62 (95% CI, 1.16-18.22; p = 0.02) among patients with sepsis (pooled OR = 1.48).

CONCLUSIONS

There is significant among-hospital variation in ICU admission rates for patients with sepsis not requiring life support therapies, how sensitive those ICU admission decisions are to hospital capacity strain, and the association of ICU admission with hospital LOS and hospital mortality. Hospital-level heterogeneity should be considered alongside patient-level heterogeneity in critical and acute care study design and interpretation.

Predicting short-term outcomes after transcatheter aortic valve replacement for aortic stenosis

BACKGROUND

The approved use of transcatheter aortic valve replacement (TAVR) for aortic stenosis has expanded substantially over time. However, gaps remain with respect to accurately delineating risk for poor clinical and patient-centered outcomes. Our objective was to develop prediction models for 30-day clinical and patient-centered outcomes after TAVR within a large, diverse community-based population.

METHODS

We identified all adults who underwent TAVR between 2013-2019 at Kaiser Permanente Northern California, an integrated healthcare delivery system, and were monitored for the following 30-day outcomes: all-cause death, improvement in quality of life, all-cause hospitalizations, all-cause emergency department (ED) visits, heart failure (HF)-related hospitalizations, and HF-related ED visits. We developed prediction models using gradient boosting machines using linked demographic, clinical and other data from the Society for Thoracic Surgeons (STS)/American College of Cardiology (ACC) TVT Registry and electronic health records. We evaluated model performance using area under the curve (AUC) for model discrimination and associated calibration plots. We also evaluated the association of individual predictors with outcomes using logistic regression for quality of life and Cox proportional hazards regression for all other outcomes.

RESULTS

We identified 1,565 eligible patients who received TAVR. The risks of adverse 30-day post-TAVR outcomes ranged from 1.3% (HF hospitalizations) to 15.3% (all-cause ED visits). In models with the highest discrimination, discrimination was only moderate for death (AUC 0.60) and quality of life (AUC 0.62), but better for HF-related ED visits (AUC 0.76). Calibration also varied for different outcomes. Importantly, STS risk score only independently predicted death and all-cause hospitalization but no other outcomes. Older age also only independently predicted HF-related ED visits, and race/ethnicity was not significantly associated with any outcomes.

CONCLUSIONS

Despite using a combination of detailed STS/ACC TVT Registry and electronic health record data, predicting short-term clinical and patient-centered outcomes after TAVR remains challenging. More work is needed to identify more accurate predictors for post-TAVR outcomes to support personalized clinical decision making and monitoring strategies.

Prediction of in-hospital mortality among intensive care unit patients using modified daily Laboratory-based Acute Physiology Scores, version 2 (LAPS2)

Background

Mortality prediction for intensive care unit (ICU) patients frequently relies on single acuity measures based on ICU admission physiology without accounting for subsequent clinical changes.

Objectives

Evaluate novel models incorporating modified admission and daily, time-updating Laboratory-based Acute Physiology Scores, version 2 (LAPS2) to predict in-hospital mortality among ICU patients.

Research design

Retrospective cohort study.

Subjects

All ICU patients in five hospitals from October 2017 through September 2019.

Measures

We used logistic regression, penalized logistic regression, and random forest models to predict in-hospital mortality within 30 days of ICU admission using admission LAPS2 alone in patient-level and patient-day-level models, or admission and daily LAPS2 at the patient-day level. Multivariable models included patient and admission characteristics. We performed internal-external validation using four hospitals for training and the fifth for validation, repeating analyses for each hospital as the validation set. We assessed performance using scaled Brier scores (SBS), c-statistics, and calibration plots.

Results

The cohort included 13,993 patients and 120,101 ICU days. The patient-level model including the modified admission LAPS2 without daily LAPS2 had an SBS of 0.175 (95% CI 0.148-0.201) and c-statistic of 0.824 (95% CI 0.808-0.840). Patient-day-level models including daily LAPS2 consistently outperformed models with modified admission LAPS2 alone. Among patients with <50% predicted mortality, daily models were better calibrated than models with modified admission LAPS2 alone.

Conclusions

Models incorporating daily, time-updating LAPS2 to predict mortality among an ICU population perform as well or better than models incorporating modified admission LAPS2 alone.

Risk of hospitalization in a sample of COVID-19 patients with and without chronic obstructive pulmonary disease

BACKGROUND AND OBJECTIVE

Patients with chronic obstructive pulmonary disease (COPD) may have worse coronavirus disease-2019 (COVID-19)-related outcomes. We compared COVID-19 hospitalization risk in patients with and without COPD.

METHODS

This retrospective cohort study included patients ≥40 years, SARS-CoV-2 positive, and with Kaiser Permanente Northern California membership ≥1 year before COVID-19 diagnosis (electronic health records and claims data). COVID-19-related hospitalization risk was assessed by sequentially adjusted logistic regression models and stratified by disease severity. Secondary outcome was death/hospice referral after COVID-19.

RESULTS AND DISCUSSION

Of 19,558 COVID-19 patients, 697 (3.6%) had COPD. Compared with patients without COPD, COPD patients were older (median age: 69 vs 53 years); had higher Elixhauser Comorbidity Index (5 vs 0) and more median baseline outpatient (8 vs 4), emergency department (2 vs 1), and inpatient (2 vs 1) encounters. Unadjusted analyses showed increased odds of hospitalization with COPD (odds ratio [OR]: 3.93; 95% confidence interval [CI]: 3.40-4.60). After full risk adjustment, there were no differences in odds of hospitalization (OR: 1.14, 95% CI: 0.93-1.40) or death/hospice referral (OR: 0.96, 95% CI: 0.72-1.27) between patients with and without COPD. Primary/secondary outcomes did not differ by COPD severity, except for higher odds of hospitalization in COPD patients requiring supplemental oxygen versus those without COPD (OR: 1.84, 95% CI: 1.02-3.33).

CONCLUSIONS

Except for hospitalization among patients using supplemental oxygen, no differences in odds of hospitalization or death/hospice referral were observed in the COVID-19 patient sample depending on whether they had COPD.

Characterizing Equity of Intensive Care Unit Admissions for Sepsis and Acute Respiratory Failure

Rationale: Patients who identify as from racial or ethnic minority groups who have sepsis or acute respiratory failure (ARF) experience worse outcomes relative to nonminority patients, but processes of care accounting for disparities are not well-characterized. Objectives: Determine whether reductions in intensive care unit (ICU) admission during hospital-wide capacity strain occur preferentially among patients who identify with racial or ethnic minority groups. Methods: This retrospective cohort among 27 hospitals across the Philadelphia metropolitan area and Northern California between 2013 and 2018 included adult patients with sepsis and/or ARF who did not require life support at the time of hospital admission. An updated model of hospital-wide capacity strain was developed that permitted determination of relationships between patient race, ethnicity, ICU admission, and strain. Results: After adjustment for demographics, disease severity, and study hospital, patients who identified as Asian or Pacific Islander had the highest adjusted ICU admission odds relative to patients who identified as White in both the sepsis and ARF populations (odds ratio, 1.09; P = 0.006 and 1.26; P < 0.001). ICU admission was also elevated for patients with ARF who identified as Hispanic (odds ratio, 1.11; P = 0.020). Capacity strain did not modify differences in ICU admission for patients who identified with a minority group in either disease population (all interactions, P > 0.05). Conclusions: Systematic differences in ICU admission patterns were observed for patients that identified as Asian, Pacific Islander, and Hispanic. However, ICU admission was not restricted from these groups, and capacity strain did not preferentially reduce ICU admission from patients identifying with minority groups. Further characterization of provider decision-making can help contextualize these findings as the result of disparate decision-making or a mechanism of equitable care.

Risk-Adjusting Mortality in the Nationwide Veterans Affairs Healthcare System

BACKGROUND

The US Veterans Affairs (VA) healthcare system began reporting risk-adjusted mortality for intensive care (ICU) admissions in 2005. However, while the VA's mortality model has been updated and adapted for risk-adjustment of all inpatient hospitalizations, recent model performance has not been published. We sought to assess the current performance of VA's 4 standardized mortality models: acute care 30-day mortality (acute care SMR-30); ICU 30-day mortality (ICU SMR-30); acute care in-hospital mortality (acute care SMR); and ICU in-hospital mortality (ICU SMR).

METHODS

Retrospective cohort study with split derivation and validation samples. Standardized mortality models were fit using derivation data, with coefficients applied to the validation sample. Nationwide VA hospitalizations that met model inclusion criteria during fiscal years 2017-2018(derivation) and 2019 (validation) were included. Model performance was evaluated using c-statistics to assess discrimination and comparison of observed versus predicted deaths to assess calibration.

RESULTS

Among 1,143,351 hospitalizations eligible for the acute care SMR-30 during 2017-2019, in-hospital mortality was 1.8%, and 30-day mortality was 4.3%. C-statistics for the SMR models in validation data were 0.870 (acute care SMR-30); 0.864 (ICU SMR-30); 0.914 (acute care SMR); and 0.887 (ICU SMR). There were 16,036 deaths (4.29% mortality) in the SMR-30 validation cohort versus 17,458 predicted deaths (4.67%), reflecting 0.38% over-prediction. Across deciles of predicted risk, the absolute difference in observed versus predicted percent mortality was a mean of 0.38%, with a maximum error of 1.81% seen in the highest-risk decile.

CONCLUSIONS AND RELEVANCE

The VA's SMR models, which incorporate patient physiology on presentation, are highly predictive and demonstrate good calibration both overall and across risk deciles. The current SMR models perform similarly to the initial ICU SMR model, indicating appropriate adaption and re-calibration.

Quantifying the breadth of antibiotic exposure in sepsis and suspected infection using spectrum scores

A retrospective cohort study. Studies to quantify the breadth of antibiotic exposure across populations remain limited. Therefore, we applied a validated method to describe the breadth of antimicrobial coverage in a multicenter cohort of patients with suspected infection and sepsis. We conducted a retrospective cohort study across 21 hospitals within an integrated healthcare delivery system of patients admitted to the hospital through the ED with suspected infection or sepsis and receiving antibiotics during hospitalization from January 1, 2012, to December 31, 2017. We quantified the breadth of antimicrobial coverage using the Spectrum Score, a numerical score from 0 to 64, in patients with suspected infection and sepsis using electronic health record data. Of 364,506 hospital admissions through the emergency department, we identified 159,004 (43.6%) with suspected infection and 205,502 (56.4%) with sepsis. Inpatient mortality was higher among those with sepsis compared to those with suspected infection (8.4% vs 1.2%; P < .001). Patients with sepsis had higher median global Spectrum Scores (43.8 [interquartile range IQR 32.0-49.5] vs 43.5 [IQR 26.8-47.2]; P < .001) and additive Spectrum Scores (114.0 [IQR 57.0-204.5] vs 87.5 [IQR 45.0-144.8]; P < .001) compared to those with suspected infection. Increased Spectrum Scores were associated with inpatient mortality, even after covariate adjustments (adjusted odds ratio per 10-point increase in Spectrum Score 1.31; 95%CI 1.29-1.33). Spectrum Scores quantify the variability in antibiotic breadth among individual patients, between suspected infection and sepsis populations, over the course of hospitalization, and across infection sources. They may play a key role in quantifying the variation in antibiotic prescribing in patients with suspected infection and sepsis.

Effect of Medically Tailored Meals on Clinical Outcomes in Recently Hospitalized High-Risk Adults

BACKGROUND

Inability to adhere to nutritional recommendations is common and linked to worse outcomes in patients with nutrition-sensitive conditions.

OBJECTIVES

The purpose of this study is to evaluate whether medically tailored meals (MTMs) improve outcomes in recently discharged adults with nutrition-sensitive conditions compared with usual care.

RESEARCH DESIGN

Remote pragmatic randomized trial.

SUBJECTS

Adults with heart failure, diabetes, or chronic kidney disease being discharged home between April 27, 2020, and June 9, 2021, from 5 hospitals within an integrated health care delivery system.

MEASURES

Participants were prerandomized to 10 weeks of MTMs (with or without virtual nutritional counseling) compared with usual care. The primary outcome was all-cause hospitalization within 90 days after discharge. Exploratory outcomes included all-cause and cause-specific health care utilization and all-cause death within 90 days after discharge.

RESULTS

A total of 1977 participants (MTMs: n=993, with 497 assigned to also receive virtual nutritional counseling; usual care: n=984) were enrolled. Compared with usual care, MTMs did not reduce all-cause hospitalization at 90 days after discharge [adjusted hazard ratio, aHR: 1.02, 95% confidence interval (CI), 0.86-1.21]. In exploratory analyses, MTMs were associated with lower mortality (aHR: 0.65, 95% CI, 0.43-0.98) and fewer hospitalizations for heart failure (aHR: 0.53, 95% CI, 0.33-0.88), but not for any emergency department visits (aHR: 0.95, 95% CI, 0.78-1.15) or diabetes-related hospitalizations (aHR: 0.75, 95% CI, 0.31-1.82). No additional benefit was observed with virtual nutritional counseling.

CONCLUSIONS

Provision of MTMs after discharge did not reduce risk of all-cause hospitalization in adults with nutrition-sensitive conditions. Additional large-scale randomized controlled trials are needed to definitively determine the impact of MTMs on survival and cause-specific health care utilization in at-risk individuals.

Adults hospitalized with breakthrough COVID-19 have lower mortality than matched unvaccinated adults

BACKGROUND

Coronavirus disease 2019 (COVID-19) breakthrough infections are common.

OBJECTIVE

Evaluate in-hospital mortality of patients with COVID-19 by vaccination status using retrospective cohort study.

METHODS

We generated propensity scores for receipt of full vaccination in adults requiring supplemental oxygen hospitalized at Kaiser Permanente Northern California (1 April 2021 to 30 November 2021) with positive severe acute respiratory syndrome coronavirus 2 polymerase chain reaction tests. Optimal matching of fully vaccinated/unvaccinated patients was performed comparing in-hospital mortality.

RESULTS

Of 7305 patients, 1463 (20.0%) were full, 138 (1.9%) were partial, and 5704 (78.1%) were unvaccinated. Fully vaccinated were older than partial or unvaccinated (71.0, 63.0, and 54.0 years, respectively, p < 0.001) with more comorbidities (Comorbidity Point Scores 33.0, 22.0, and 10.0, p < 0.001) and immunosuppressant (11.5%, 8.7%, and 3.0%, p < 0.001) or chemotherapy exposure (2.8%, 0.7%, and 0.4%, p < 0.001). Fewer fully vaccinated patients died compared to matched unvaccinated (9.0% vs. 16.3%, p < 0.0001).

CONCLUSION

Fully vaccinated patients are less likely to die compared to matched unvaccinated patients.

Artificial intelligence and clinical deterioration

PURPOSE OF REVIEW

To provide an overview of the systems being used to identify and predict clinical deterioration in hospitalised patients, with focus on the current and future role of artificial intelligence (AI).

RECENT FINDINGS

There are five leading AI driven systems in this field: the Advanced Alert Monitor (AAM), the electronic Cardiac Arrest Risk Triage (eCART) score, Hospital wide Alert Via Electronic Noticeboard, the Mayo Clinic Early Warning Score, and the Rothman Index (RI). Each uses Electronic Patient Record (EPR) data and machine learning to predict adverse events. Less mature but relevant evolutions are occurring in the fields of Natural Language Processing, Time and Motion Studies, AI Sepsis and COVID-19 algorithms.

SUMMARY

Research-based AI-driven systems to predict clinical deterioration are increasingly being developed, but few are being implemented into clinical workflows. Escobar et al. (AAM) provide the current gold standard for robust model development and implementation methodology. Multiple technologies show promise, however, the pathway to meaningfully affect patient outcomes remains challenging.

Effect of Electronic and Mail Outreach From Primary Care Physicians for COVID-19 Vaccination of Black and Latino Older Adults: A Randomized Clinical Trial

Importance

COVID-19 morbidity is highest in Black and Latino older adults. These racial and ethnic groups initially had lower vaccination uptake than others, and rates in Black adults continue to lag.

Objectives

To evaluate the effect of outreach via electronic secure messages and mailings from primary care physicians (PCPs) on COVID-19 vaccination uptake among Black and Latino older adults and to compare the effects of culturally tailored and standard PCP messages.

Design, Setting, and Participants

This randomized clinical trial was conducted from March 29 to May 20, 2021, with follow-up surveys through July 31, 2021. Latino and Black individuals aged 65 years and older from 4 Kaiser Permanente Northern California (KPNC) service areas were included. Data were analyzed from May 27, 2021, to September 28, 2021.

Interventions

Individuals who had not received COVID-19 vaccination after previous outreach were randomized to electronic secure message and/or mail outreach from their PCP, similar outreach with additional culturally tailored content, or usual care. Outreach groups were sent a secure message or letter in their PCP's name, followed by a postcard to those still unvaccinated after 4 weeks.

Main Outcomes and Measures

The primary outcome was time to receipt of COVID-19 vaccination during the 8 weeks after initial study outreach. KPNC data were supplemented with state data from external sources. Intervention effects were evaluated via proportional hazards regression.

Results

Of 8287 included individuals (mean [SD] age, 72.6 [7.0] years; 4665 [56.3%] women), 2434 (29.4%) were Black, 3782 (45.6%) were Latino and preferred English-language communications, and 2071 (25.0%) were Latino and preferred Spanish-language communications; 2847 participants (34.4%) had a neighborhood deprivation index at the 75th percentile or higher. A total of 2767 participants were randomized to culturally tailored PCP outreach, 2747 participants were randomized to standard PCP outreach, and 2773 participants were randomized to usual care. Culturally tailored PCP outreach led to higher COVID-19 vaccination rates during follow-up compared with usual care (664 participants [24.0%] vs 603 participants [21.7%]; adjusted hazard ratio (aHR), 1.22; 95% CI, 1.09-1.37), as did standard PCP outreach (635 participants [23.1%]; aHR, 1.17; 95% CI, 1.04-1.31). Individuals who were Black (aHR, 1.19; 95% CI, 1.06-1.33), had high neighborhood deprivation (aHR, 1.17; 95% CI, 1.03-1.33), and had medium to high comorbidity scores (aHR, 1.19; 95% CI, 1.09-1.31) were more likely to be vaccinated during follow-up.

Conclusions and Relevance

This randomized clinical trial found that PCP outreach using electronic and mailed messages increased COVID-19 vaccination rates among Black and Latino older adults.

Trial Registration

ClinicalTrials.gov Identifier: NCT05096026.

Risk of Cardiovascular Disease in Women With and Without Breast Cancer: The Pathways Heart Study

PURPOSE

To examine cardiovascular disease (CVD) and mortality risk in women with breast cancer (BC) by cancer therapy received relative to women without BC.

METHODS

The study population comprised Kaiser Permanente Northern California members. Cases with invasive BC diagnosed from 2005 to 2013 were matched 1:5 to controls without BC on birth year and race/ethnicity. Cancer treatment, CVD outcomes, and covariate data were from electronic health records. Multivariable Cox proportional hazards models estimated hazard ratios (HRs) and 95% CIs of CVD incidence and mortality by receipt of chemotherapy treatment combinations, radiation therapy, and endocrine therapy.

RESULTS

A total of 13,642 women with BC were matched to 68,202 controls without BC. Over a 7-year average follow-up (range < 1-14 years), women who received anthracyclines and/or trastuzumab had high risk of heart failure/cardiomyopathy relative to controls, with the highest risk seen in women who received both anthracyclines and trastuzumab (HR, 3.68; 95% CI, 1.79 to 7.59). High risk of heart failure and/or cardiomyopathy was also observed in women with BC with a history of radiation therapy (HR, 1.38; 95% CI, 1.13 to 1.69) and aromatase inhibitor use (HR, 1.31; 95% CI, 1.07 to 1.60), relative to their controls. Elevated risks for stroke, arrhythmia, cardiac arrest, venous thromboembolic disease, CVD-related death, and death from any cause were also observed in women with BC on the basis of cancer treatment received.

CONCLUSION

Women with BC had increased incidence of CVD events, CVD-related mortality, and all-cause mortality compared with women without BC, and risks varied according to the history of cancer treatment received. Studies are needed to determine how women who received BC treatment should be cared for to improve cardiovascular outcomes.

Exploratory analysis of novel electronic health record variables for quantification of healthcare delivery strain, prediction of mortality, and prediction of imminent discharge

OBJECTIVE

To explore the relationship between novel, time-varying predictors for healthcare delivery strain (eg, counts of patient orders per hour) and imminent discharge and in-hospital mortality.

MATERIALS AND METHODS

We conducted a retrospective cohort study using data from adults hospitalized at 21 Kaiser Permanente Northern California hospitals between November 1, 2015 and October 31, 2020 and the nurses caring for them. Patient data extracted included demographics, diagnoses, severity measures, occupancy metrics, and process of care metrics (eg, counts of intravenous drip orders per hour). We linked these data to individual registered nurse records and created multiple dynamic, time-varying predictors (eg, mean acute severity of illness for all patients cared for by a nurse during a given hour). All analyses were stratified by patients' initial hospital unit (ward, stepdown unit, or intensive care unit). We used discrete-time hazard regression to assess the association between each novel time-varying predictor and the outcomes of discharge and mortality, separately.

RESULTS

Our dataset consisted of 84 162 161 hourly records from 954 477 hospitalizations. Many novel time-varying predictors had strong associations with the 2 study outcomes. However, most of the predictors did not merely track patients' severity of illness; instead, many of them only had weak correlations with severity, often with complex relationships over time.

DISCUSSION

Increasing availability of process of care data from automated electronic health records will permit better quantification of healthcare delivery strain. This could result in enhanced prediction of adverse outcomes and service delays.

CONCLUSION

New conceptual models will be needed to use these new data elements.

Comparison of Electronic Frailty Metrics for Prediction of Adverse Outcomes of Abdominal Surgery

Importance

Electronic frailty metrics have been developed for automated frailty assessment and include the Hospital Frailty Risk Score (HFRS), the Electronic Frailty Index (eFI), the 5-Factor Modified Frailty Index (mFI-5), and the Risk Analysis Index (RAI). Despite substantial differences in their construction, these 4 electronic frailty metrics have not been rigorously compared within a surgical population.

Objective

To characterize the associations between 4 electronic frailty metrics and to measure their predictive value for adverse surgical outcomes.

Design, Setting, and Participants

This retrospective cohort study used electronic health record data from patients who underwent abdominal surgery from January 1, 2010, to December 31, 2020, at 20 medical centers within Kaiser Permanente Northern California (KPNC). Participants included adults older than 50 years who underwent abdominal surgical procedures at KPNC from 2010 to 2020 that were sampled for reporting to the National Surgical Quality Improvement Program.

Main Outcomes and Measures

Pearson correlation coefficients between electronic frailty metrics and area under the receiver operating characteristic curve (AUROC) of univariate models and multivariate preoperative risk models for 30-day mortality, readmission, and morbidity, which was defined as a composite of mortality and major postoperative complications.

Results

Within the cohort of 37 186 patients, mean (SD) age, 67.9 (female, 19 127 [51.4%]), correlations between pairs of metrics ranged from 0.19 (95% CI, 0.18- 0.20) for mFI-5 and RAI 0.69 (95% CI, 0.68-0.70). Only 1085 of 37 186 (2.9%) were classified as frail based on all 4 metrics. In univariate models for morbidity, HFRS demonstrated higher predictive discrimination (AUROC, 0.71; 95% CI, 0.70-0.72) than eFI (AUROC, 0.64; 95% CI, 0.63-0.65), mFI-5 (AUROC, 0.58; 95% CI, 0.57-0.59), and RAI (AUROC, 0.57; 95% CI, 0.57-0.58). The predictive discrimination of multivariate models with age, sex, comorbidity burden, and procedure characteristics for all 3 adverse surgical outcomes improved by including HFRS into the models.

Conclusions and Relevance

In this cohort study, the 4 electronic frailty metrics demonstrated heterogeneous correlation and classified distinct groups of surgical patients as frail. However, HFRS demonstrated the highest predictive value for adverse surgical outcomes.

Development and Structure of an Accurate Machine Learning Algorithm to Predict Inpatient Mortality and Hospice Outcomes in the Coronavirus Disease 2019 Era

BACKGROUND

The coronavirus disease 2019 (COVID-19) pandemic has challenged the accuracy and racial biases present in traditional mortality scores. An accurate prognostic model that can be applied to hospitalized patients irrespective of race or COVID-19 status may benefit patient care.

RESEARCH DESIGN

This cohort study utilized historical and ongoing electronic health record features to develop and validate a deep-learning model applied on the second day of admission predicting a composite outcome of in-hospital mortality, discharge to hospice, or death within 30 days of admission. Model features included patient demographics, diagnoses, procedures, inpatient medications, laboratory values, vital signs, and substance use history. Conventional performance metrics were assessed, and subgroup analysis was performed based on race, COVID-19 status, and intensive care unit admission.

SUBJECTS

A total of 35,521 patients hospitalized between April 2020 and October 2020 at a single health care system including a tertiary academic referral center and 9 community hospitals.

RESULTS

Of 35,521 patients, including 9831 non-White patients and 2020 COVID-19 patients, 2838 (8.0%) met the composite outcome. Patients who experienced the composite outcome were older (73 vs. 61 y old) with similar sex and race distributions between groups. The model achieved an area under the receiver operating characteristic curve of 0.89 (95% confidence interval: 0.88, 0.91) and an average positive predictive value of 0.46 (0.40, 0.52). Model performance did not differ significantly in White (0.89) and non-White (0.90) subgroups or when grouping by COVID-19 status and intensive care unit admission.

CONCLUSION

A deep-learning model using large-volume, structured electronic health record data can effectively predict short-term mortality or hospice outcomes on the second day of admission in the general inpatient population without significant racial bias.

Prospective evaluation of social risks, physical function, and cognitive function in prediction of non-elective rehospitalization and post-discharge mortality

Background

Increasing evidence suggests that social factors and problems with physical and cognitive function may contribute to patients’ rehospitalization risk. Understanding a patient’s readmission risk may help healthcare providers develop tailored treatment and post-discharge care plans to reduce readmission and mortality. This study aimed to evaluate whether including patient-reported data on social factors; cognitive status; and physical function improves on a predictive model based on electronic health record (EHR) data alone.

Methods

We conducted a prospective study of 1,547 hospitalized adult patients in 3 Kaiser Permanente Northern California hospitals. The main outcomes were non-elective rehospitalization or death within 30 days post-discharge. Exposures included patient-reported social factors and cognitive and physical function (obtained in a pre-discharge interview) and EHR–derived data for comorbidity burden, acute physiology, care directives, prior utilization, and hospital length of stay. We performed bivariate comparisons using Chi-square, t-tests, and Wilcoxon rank-sum tests and assessed correlations between continuous variables using Spearman’s rho statistic. For all models, the results reported were obtained after fivefold cross validation.

Results

The 1,547 adult patients interviewed were younger (age, p = 0.03) and sicker (COPS2, p < 0.0001) than the rest of the hospitalized population. Of the 6 patient-reported social factors measured, 3 (not living with a spouse/partner, transportation difficulties, health or disability-related limitations in daily activities) were significantly associated (p < 0.05) with the main outcomes, while 3 (living situation concerns, problems with food availability, financial problems) were not. Patient-reported cognitive (p = 0.027) and physical function (p = 0.01) were significantly lower in patients with the main outcomes. None of the patient-reported variables, singly or in combination, improved predictive performance of a model that included acute physiology and longitudinal comorbidity burden (area under the receiver operator characteristic curve was 0.716 for both the EHR model and maximal performance of a random forest model including all predictors).

Conclusions

In this insured population, incorporating patient-reported social factors and measures of cognitive and physical function did not improve performance of an EHR-based model predicting 30-day non-elective rehospitalization or mortality. While incorporating patient-reported social and functional status data did not improve ability to predict these outcomes, such data may still be important for improving patient outcomes.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12913-022-07910-w.

Prognostic Accuracy of Presepsis and Intrasepsis Characteristics for Prediction of Cardiovascular Events After a Sepsis Hospitalization

Sepsis survivors face increased risk for cardiovascular complications; however, the contribution of intrasepsis events to cardiovascular risk profiles is unclear.

Evaluation of Outcomes Following Hospital-Wide Implementation of a Subcutaneous Insulin Protocol for Diabetic Ketoacidosis

Importance

Standard diabetic ketoacidosis care in the US includes intravenous insulin treatment in the intensive care unit. Subcutaneous (SQ) insulin could decrease intensive care unit need, but the data are limited.

Objective

To assess outcomes after implementation of an SQ insulin protocol for treating diabetic ketoacidosis.

Design, Setting, and Participants

This cohort study is a retrospective evaluation of a prospectively implemented SQ insulin protocol. The study was conducted at an integrated health care system in Northern California. Participants included hospitalized patients with diabetic ketoacidosis at 21 hospitals between January 1, 2010, and December 31, 2019. The preimplementation phase was 2010 to 2015, and the postimplementation phase was 2017 to 2019. Data analysis was performed from October 2020 to January 2022.

Exposure

An SQ insulin treatment protocol for diabetic ketoacidosis.

Main Outcomes and Measures

Difference-in-differences evaluation of the need for intensive care, mortality, readmission, and length of stay at a single intervention site using an SQ insulin protocol from 2017 onward compared with 20 control hospitals using standard care.

Results

A total of 7989 hospitalizations for diabetic ketoacidosis occurred, with 4739 (59.3%) occurring before and 3250 (40.7%) occurring after implementation. The overall mean (SD) age was 42.3 (17.7) years, with 4137 hospitalizations (51.8%) occurring among female patients. Before implementation, SQ insulin was the first insulin used in 40 intervention (13.4%) and 651 control (14.7%) hospitalizations. After implementation, 98 hospitalizations (80.3%) received SQ insulin first at the intervention site compared with 402 hospitalizations (12.8%) at control sites. The adjusted rate ratio for intensive care unit admission was 0.43 (95% CI, 0.33-0.56) at the intervention sites, a 57% reduction compared with control sites, and was 0.50 (95% CI, 0.25-0.99) for 30-day hospital readmission, a 50% reduction. There were no significant changes in hospital length of stay and rates of death.

Conclusions and Relevance

These findings suggest that a protocol based on SQ insulin for diabetic ketoacidosis treatment was associated with significant decreases in intensive care unit need and readmission, with no evidence of increases in adverse events.

Association of ICU Admission and Outcomes in Sepsis and Acute Respiratory Failure

Rationale: Many decisions to admit patients to the ICU are not grounded in evidence regarding who benefits from such triage, straining ICU capacity and limiting its cost-effectiveness. Objectives: To measure the benefits of ICU admission for patients with sepsis or acute respiratory failure. Methods: At 27 United States hospitals across two health systems from 2013 to 2018, we performed a retrospective cohort study using two-stage instrumental variable quantile regression with a strong instrument (hospital capacity strain) governing ICU versus ward admission among high-acuity patients (i.e., laboratory-based acute physiology score v2 ⩾ 100) with sepsis and/or acute respiratory failure who did not require mechanical ventilation or vasopressors in the emergency department. Measurements and Main Results: Among patients with sepsis (n = 90,150), admission to the ICU was associated with a 1.32-day longer hospital length of stay (95% confidence interval [CI], 1.01-1.63; P < 0.001) (when treating deaths as equivalent to long lengths of stay) and higher in-hospital mortality (odds ratio, 1.48; 95% CI, 1.13-1.88; P = 0.004). Among patients with respiratory failure (n = 45,339), admission to the ICU was associated with a 0.82-day shorter hospital length of stay (95% CI, -1.17 to -0.46; P < 0.001) and reduced in-hospital mortality (odds ratio, 0.75; 95% CI, 0.57-0.96; P = 0.04). In sensitivity analyses of length of stay, excluding, ignoring, or censoring death, results were similar in sepsis but not in respiratory failure. In subgroup analyses, harms of ICU admission for patients with sepsis were concentrated among older patients and those with fewer comorbidities, and the benefits of ICU admission for patients with respiratory failure were concentrated among older patients, highest-acuity patients, and those with more comorbidities. Conclusions: Among high-acuity patients with sepsis who did not require life support in the emergency department, initial admission to the ward, compared with the ICU, was associated with shorter length of stay and improved survival, whereas among patients with acute respiratory failure, triage to the ICU compared with the ward was associated with improved survival.

Validation of Respiratory Rate-Oxygenation Index in Patients With COVID-19-Related Respiratory Failure

OBJECTIVES:

The respiratory rate-oxygenation (ROX) index is a fraction of oxygen saturation, Fio₂, and respiratory rate that has been validated to predict receipt of invasive mechanical ventilation in patients receiving high-flow nasal cannula (HFNC). This study aimed to validate ROX in a cohort of inpatients with COVID-19–related respiratory failure.

DESIGN:

Retrospective validation of the ROX index. We calculated sensitivity, specificity, positive predictive value, negative predictive value, and 95% CIs of ROX for invasive mechanical ventilation any time during hospitalization.

SETTING:

Twenty-one hospitals of Kaiser Permanente Northern California, an integrated healthcare delivery system.

PATIENTS:

We identified adults with positive severe acute respiratory syndrome coronavirus 2 polymerase chain reaction test within 3 weeks of, or during, hospitalization between February 1, 2020, and December 31, 2020. We calculated ROX at 12 hours after HFNC initiation. We grouped patients as low (≥ 4.88), intermediate (< 4.88 and ≥ 3.85), or high (< 3.85) risk using previously published thresholds.

INTERVENTIONS:

None.

MEASUREMENTS AND MAIN RESULTS:

We identified 1,847 patients who had no limitation of life support. Of these, 525 (31.7%) received invasive mechanical ventilation any time during hospitalization and 511 died (27.7%). The sensitivity, specificity, positive predictive value, and negative predictive value of 12-hour ROX threshold (< 3.85) predicting invasive mechanical ventilation were 32.3% (95% CI, 28.5–36.3%), 89.8% (95% CI, 88.0–91.4%), 59.4% (95% CI, 53.8–64.9%), and 74.1% (95% CI, 71.8–76.3%), respectively.

CONCLUSIONS:

The 12-hour ROX index has a positive predictive value (59.4%) using threshold of less than 3.85 for COVID-19 patients needing invasive mechanical ventilation. Our health system has embedded ROX into the electronic health record to prioritize rounding during periods of inpatient surge.

Food security, diet quality, nutritional knowledge, and attitudes towards research in adults with heart failure during the COVID-19 pandemic

BACKGROUND

The impact of the novel coronavirus disease 2019 (COVID-19) pandemic on diet and nutrition among older adults with chronic medical conditions have not been well-described.

METHODS

We conducted a survey addressing (1) food access, (2) diet quality and composition, (3) nutritional understanding, and (4) attitudes towards research among adults with heart failure (HF) within an integrated health system. Adults (≥18 years) with diagnosed HF and at least one prior hospitalization for HF within the last 12 months were approached to complete the survey electronically or by mail. Outcomes included all-cause and HF-specific hospitalizations and all-cause death was ascertained via the electronic health record.

RESULTS

Among 1212 survey respondents (32.5% of eligible patients) between May 18, 2020 and September 30, 2020, mean ± SD age was 77.9 ± 11.4 years, 50.1% were women, and median (25th-75th) left ventricular ejection fraction was 55% (40%-60%). Overall, 15.1% of respondents were food insecure, and only 65% of participants answered correctly more than half of the items assessing nutritional knowledge. Although most respondents were willing to participate in future research, that number largely declined for studies requiring blood draws (32.2%), study medication (14.4%), and/or behavior change (27.1%). Food security, diet quality, and nutritional knowledge were not independently associated with outcomes at 90 or 180 days.

CONCLUSION

In a cohort of older adults with HF and multiple comorbidities, a significant proportion reported issues with food access, diet quality, and nutritional knowledge during the COVID-19 pandemic. Future research should evaluate interventions targeting these domains in at-risk individuals.