Consensus modeling: Safer transfer learning for small health systems.

Predictive modeling is becoming an essential tool for clinical decision support, but health systems with smaller sample sizes may construct suboptimal or overly specific models. Models become over-specific when beside true physiological effects, they also incorporate potentially volatile site-specific artifacts. These artifacts can change suddenly and can render the model unsafe. To obtain safer models, health systems with inadequate sample sizes may adopt one of the following options. First, they can use a generic model, such as one purchased from a vendor, but often such a model is not sufficiently specific to the patient population and is thus suboptimal. Second, they can participate in a research network. Paradoxically though, sites with smaller datasets contribute correspondingly less to the joint model, again rendering the final model suboptimal. Lastly, they can use transfer learning, starting from a model trained on a large data set and updating this model to the local population. This strategy can also result in a model that is over-specific. In this paper we present the consensus modeling paradigm, which uses the help of a large site (source) to reach a consensus model at the small site (target). We evaluate the approach on predicting postoperative complications at two health systems with 9,044 and 38,045 patients (rare outcomes at about 1% positive rate), and conduct a simulation study to understand the performance of consensus modeling relative to the other three approaches as a function of the available training sample size at the target site. We found that consensus modeling exhibited the least over-specificity at either the source or target site and achieved the highest combined predictive performance.

Artificial intelligence of arterial Doppler waveforms to predict major adverse outcomes among patients with diabetes mellitus.

OBJECTIVE: Patients with diabetes mellitus (DM) are at increased risk for peripheral artery disease (PAD) and its complications. Arterial calcification and non-compressibility may limit test interpretation in this population. Developing tools capable of identifying PAD and predicting major adverse cardiac event (MACE) and limb event (MALE) outcomes among patients with DM would be clinically useful. Deep neural network analysis of resting Doppler arterial waveforms was used to detect PAD among patients with DM and to identify those at greatest risk for major adverse outcome events. METHODS: Consecutive patients with DM undergoing lower limb arterial testing (April 1, 2015-December 30, 2020) were randomly allocated to training, validation, and testing subsets (60%, 20%, and 20%). Deep neural networks were trained on resting posterior tibial arterial Doppler waveforms to predict all-cause mortality, MACE, and MALE at 5 years using quartiles based on the distribution of the prediction score. RESULTS: Among 11,384 total patients, 4211 patients with DM met study criteria (mean age, 68.6 ± 11.9 years; 32.0% female). After allocating the training and validation subsets, the final test subset included 856 patients. During follow-up, there were 262 deaths, 319 MACE, and 99 MALE. Patients in the upper quartile of prediction based on deep neural network analysis of the posterior tibial artery waveform provided independent prediction of death (hazard ratio [HR], 3.58; 95% confidence interval [CI], 2.31-5.56), MACE (HR, 2.06; 95% CI, 1.49-2.91), and MALE (HR, 13.50; 95% CI, 5.83-31.27). CONCLUSIONS: An artificial intelligence enabled analysis of a resting Doppler arterial waveform permits identification of major adverse outcomes including all-cause mortality, MACE, and MALE among patients with DM.

Creating an atlas of normal tissue for pruning WSI patching through anomaly detection.

Patching whole slide images (WSIs) is an important task in computational pathology. While most of them are designed to classify or detect the presence of pathological lesions in a WSI, the confounding role and redundant nature of normal histology are generally overlooked. In this paper, we propose and validate the concept of an "atlas of normal tissue" solely using samples of WSIs obtained from normal biopsies. Such atlases can be employed to eliminate normal fragments of tissue samples and hence increase the representativeness of the remaining patches. We tested our proposed method by establishing a normal atlas using 107 normal skin WSIs and demonstrated how established search engines like Yottixel can be improved. We used 553 WSIs of cutaneous squamous cell carcinoma to demonstrate the advantage. We also validated our method applied to an external dataset of 451 breast WSIs. The number of selected WSI patches was reduced by 30% to 50% after utilizing the proposed normal atlas while maintaining the same indexing and search performance in leave-one-patient-out validation for both datasets. We show that the proposed concept of establishing and using a normal atlas shows promise for unsupervised selection of the most representative patches of the abnormal WSI patches.

Artificial Intelligence of Arterial Doppler Waveforms to Predict Major Adverse Outcomes Among Patients Evaluated for Peripheral Artery Disease.

BACKGROUND: Patients with peripheral artery disease are at increased risk for major adverse cardiac events, major adverse limb events, and all-cause death. Developing tools capable of identifying those patients with peripheral artery disease at greatest risk for major adverse events is the first step for outcome prevention. This study aimed to determine whether computer-assisted analysis of a resting Doppler waveform using deep neural networks can accurately identify patients with peripheral artery disease at greatest risk for adverse outcome events. METHODS AND RESULTS: Consecutive patients (April 1, 2015, to December 31, 2020) undergoing ankle-brachial index testing were included. Patients were randomly allocated to training, validation, and testing subsets (60%/20%/20%). Deep neural networks were trained on resting posterior tibial arterial Doppler waveforms to predict major adverse cardiac events, major adverse limb events, and all-cause death at 5 years. Patients were then analyzed in groups based on the quartiles of each prediction score in the training set. Among 11 384 total patients, 10 437 patients met study inclusion criteria (mean age, 65.8±14.8 years; 40.6% women). The test subset included 2084 patients. During 5 years of follow-up, there were 447 deaths, 585 major adverse cardiac events, and 161 MALE events. After adjusting for age, sex, and Charlson comorbidity index, deep neural network analysis of the posterior tibial artery waveform provided independent prediction of death (hazard ratio [HR], 2.44 [95% CI, 1.78-3.34]), major adverse cardiac events (HR, 1.97 [95% CI, 1.49-2.61]), and major adverse limb events (HR, 11.03 [95% CI, 5.43-22.39]) at 5 years. CONCLUSIONS: An artificial intelligence-enabled analysis of Doppler arterial waveforms enables identification of major adverse outcomes among patients with peripheral artery disease, which may promote early adoption and adherence of risk factor modification.

Artificial intelligence for automatic detection of basal cell carcinoma from frozen tissue tangential biopsies.

Evaluation of basal cell carcinoma (BCC) involves tangential biopsies of a suspicious lesion that is sent for frozen sections and evaluated by a Mohs micrographic surgeon. Advances in artificial intelligence (AI) have made possible the development of sophisticated clinical decision support systems to provide real-time feedback to clinicians which could have a role in optimizing the diagnostic workup of BCC. There were 287 annotated whole-slide images of frozen sections from tangential biopsies, of which 121 contained BCC, that were used to train and test an AI pipeline to recognize BCC. Regions of interest were annotated by a senior dermatology resident, experienced dermatopathologist, and experienced Mohs surgeon, with concordance of annotations noted on final review. Final performance metrics included a sensitivity and specificity of 0.73 and 0.88, respectively. Our results on a relatively small dataset suggest the feasibility of developing an AI system to aid in the workup and management of BCC.

Development of the lichen planus quality of life questionnaire (LPQoL) informed by expert clinician input and patient feedback: a retrospective survey study.

Lichen planus (LP) can affect multiple body sites including skin, mucosae, scalp and nails, causing considerable impact on patients' quality of life. Currently, there are no LP patient-reported outcome measures (PROMs) that address all body sites potentially affected by LP. We developed a LP Quality of Life Questionnaire (LPQoL), informed by an expert consortium and patient survey study, to address this gap. The study was approved by our institution's Institutional Review Board. First, a 22-item LPQoL was designed with input from LP experts at our institution. The tool was then optimized by garnering input from patients recently diagnosed with LP, who were asked to complete the LPQoL, as well as the Dermatology Life Quality Index (DLQI) and a feedback form about the LPQoL. Fifty-eight of 150 patients (39% response rate) returned the questionnaire. Mean DLQI score was 4.9 ± 5.6 SD (range 0-25) and mean LPQoL score was 13.6 ± 10.4 SD (range 0-54). LPQoL score was positively correlated with DLQI score (r = 0.79; p < 0.001). Forty-nine out of 56 (88%) and 6/56 (11%) rated the LPQoL as 'very easy' or 'fairly easy' to complete, respectively. Based on participants' feedback, we increased the recall period from one week to one month and added questions on esophageal involvement. With iterative input from LP experts and patients, we developed a LPQoL to address the gap in a multi-site PROM specific to LP. This is a pilot study and there is ongoing validation studies; therefore, this measure should not be used in clinical practice or research until validated.

Postdevelopment Performance and Validation of the Artificial Intelligence-Enhanced Electrocardiogram for Detection of Cardiac Amyloidosis.

BACKGROUND: We have previously applied artificial intelligence (AI) to an electrocardiogram (ECG) to detect cardiac amyloidosis (CA). OBJECTIVES: In this validation study, the authors observe the postdevelopment performance of the AI-enhanced ECG to detect CA with respect to multiple potential confounders. METHODS: Amyloid patients diagnosed after algorithm development (June 2019-January 2022) with a 12-lead ECG were identified (n = 440) and were required to have CA. A 15:1 age- and sex-matched control group was identified (n = 6,600). Area under the receiver operating characteristic (AUC) was determined for the cohort and subgroups. RESULTS: The average age was 70.4 ± 10.3 years, 25.0% were female, and most patients were White (91.3%). In this validation, the AI-ECG for amyloidosis had an AUC of 0.84 (95% CI: 0.82-0.86) for the overall cohort and between amyloid subtypes, which is a slight decrease from the original study (AUC 0.91). White, Black, and patients of "other" races had similar algorithm performance (AUC >0.81) with a decreased performance for Hispanic patients (AUC 0.66). Algorithm performance shift over time was not observed. Low ECG voltage and infarct pattern exhibited high AUC (>0.90), while left ventricular hypertrophy and left bundle branch block demonstrated lesser performance (AUC 0.75 and 0.76, respectively). CONCLUSIONS: The AI-ECG for the detection of CA maintained an overall strong performance with respect to patient age, sex, race, and amyloid subtype. Lower performance was noted in left bundle branch block, left ventricular hypertrophy, and ethnically diverse populations emphasizing the need for subgroup-specific validation efforts.

Artificial intelligence for the evaluation of peripheral artery disease using arterial Doppler waveforms to predict abnormal ankle-brachial index.

BACKGROUND: Patients with peripheral artery disease (PAD) are at increased risk for major adverse limb and cardiac events including mortality. Developing screening tools capable of accurate PAD identification is a necessary first step for strategies of adverse outcome prevention. This study aimed to determine whether machine analysis of a resting Doppler waveform using deep neural networks can accurately identify patients with PAD. METHODS: Consecutive patients (4/8/2015 - 12/31/2020) undergoing rest and postexercise ankle-brachial index (ABI) testing were included. Patients were randomly allocated to training, validation, and testing subsets (70%/15%/15%). Deep neural networks were trained on resting posterior tibial arterial Doppler waveforms to predict normal (> 0.9) or PAD (⩽ 0.9) using rest and postexercise ABI. A separate dataset of 151 patients who underwent testing during a period after the model had been created and validated (1/1/2021 - 3/31/2021) was used for secondary validation. Area under the receiver operating characteristic curves (AUC) were constructed to evaluate test performance. RESULTS: Among 11,748 total patients, 3432 patients met study criteria: 1941 with PAD (mean age 69 ± 12 years) and 1491 without PAD (64 ± 14 years). The predictive model with highest performance identified PAD with an AUC 0.94 (CI = 0.92-0.96), sensitivity 0.83, specificity 0.88, accuracy 0.85, and positive predictive value (PPV) 0.90. Results were similar for the validation dataset: AUC 0.94 (CI = 0.91-0.98), sensitivity 0.91, specificity 0.85, accuracy 0.89, and PPV 0.89 (postexercise ABI comparison). CONCLUSION: An artificial intelligence-enabled analysis of a resting Doppler arterial waveform permits identification of PAD at a clinically relevant performance level.

Sequential organ failure assessment score improves survival prediction for left ventricular assist device recipients in intensive care.

BACKGROUND: Preoperative risk scores facilitate patient selection, but postoperative risk scores may offer valuable information for predicting outcomes. We hypothesized that the postoperative Sequential Organ Failure Assessment (SOFA) score would predict mortality after left ventricular assist device (LVAD) implantation. METHODS: We retrospectively reviewed data from 294 continuous-flow LVAD implantations performed at Mayo Clinic Rochester during 2007 to 2015. We calculated the EuroSCORE, HeartMate-II Risk Score, and RV Failure Risk Score from preoperative data and the APACHE III and Post Cardiac Surgery (POCAS) risk scores from postoperative data. Daily, maximum, and mean SOFA scores were calculated for the first 5 postoperative days. The area under receiver-operator characteristic curves (AUC) was calculated to compare the scoring systems' ability to predict 30-day, 90-day, and 1-year mortality. RESULTS: For the entire cohort, mortality was 5% at 30 days, 10% at 90 days, and 19% at 1 year. The Day 1 SOFA score had better discrimination for 30-day mortality (AUC 0.77) than the preoperative risk scores or the APACHE III and POCAS postoperative scores. The maximum SOFA score had the best discrimination for 30-day mortality (AUC 0.86), and the mean SOFA score had the best discrimination for 90-day mortality (AUC 0.82) and 1-year mortality (AUC 0.76). CONCLUSIONS: We observed that postoperative mean and maximum SOFA scores in LVAD recipients predict short-term and intermediate-term mortality better than preoperative risk scores do. However, because preoperative and postoperative risk scores each contribute unique information, they are best used in concert to predict outcomes after LVAD implantation.

Risk Prediction Model for 6-Month Mortality for Patients Discharged to Skilled Nursing Facilities.

OBJECTIVE: Hospitalized patients discharged to skilled nursing facilities (SNFs) for post-acute care are at high risk for adverse outcomes. Yet, absence of effective prognostic tools hinders optimal care planning and decision making. Our objective was to develop and validate a risk prediction model for 6-month all-cause death among hospitalized patients discharged to SNFs. DESIGN: Retrospective cohort study. SETTING AND PARTICIPANTS: Patients discharged from 1 of 2 hospitals to 1 of 10 SNFs for post-acute care in an integrated health care delivery system between January 1, 2009, and December 31, 2016. METHODS: Gradient-boosting machine modeling was used to predict all-cause death within 180 days of hospital discharge with use of patient demographic characteristics, comorbidities, pattern of prior health care use, and clinical parameters from the index hospitalization. Area under the receiver operating characteristic curve (AUC) was assessed for out-of-sample observations under 10-fold cross-validation. RESULTS: We identified 9803 unique patients with 11,647 hospital-to-SNF discharges [mean (SD) age, 80.72 (9.71) years; female sex, 61.4%]. These discharges involved 9803 patients alive at 180 days and 1844 patients who died between day 1 and day 180 of discharge. Age, comorbid burden, health care use in prior 6 months, abnormal laboratory parameters, and mobility status during hospital stay were the most important predictors of 6-month death (model AUC, 0.82). CONCLUSION AND IMPLICATIONS: We derived a robust prediction model with parameters available at discharge to SNFs to calculate risk of death within 6 months. This work may be useful to guide other clinicians wishing to develop mortality prediction instruments specific to their post-acute SNF populations.

Estimation of manual wheelchair-based activities in the free-living environment using a neural network model with inertial body-worn sensors.

Shoulder pain is common in manual wheelchair (MWC) users. Overuse is thought to be a major cause, but little is known about exposure to activities of daily living (ADLs). The study goal was to develop a method to estimate three conditions in the field: (1) non-propulsion activity, (2) MWC propulsion, and (3) static time using an inertial measurement unit (IMU). Upper arm IMU data were collected as ten MWC users performed lab-based MWC-related ADLs. A neural network model was developed to classify data as non-propulsion activity, propulsion, or static, and validated for the lab-based data collection by video comparison. Six of the participants' free-living IMU data were collected and the lab-based model was applied to estimate daily non-propulsion activity, propulsion, and static time. The neural network model yielded lab-based validity measures ≥0.87 for differentiating non-propulsion activity, propulsion, and static time. A quasi-validation of one participant's field-based data yielded validity measures ≥0.66 for identifying propulsion. Participants' estimated mean daily non-propulsion activity, propulsion, and static time ranged from 158 to 409, 13 to 25, and 367 to 609 min, respectively. The preliminary results suggest the model may be able to accurately identify MWC users' field-based activities. The inclusion of field-based IMU data in the model could further improve field-based classification.

Deep learning for dermatologists: Part II. Current applications.

Because of a convergence of the availability of large data sets, graphics-specific computer hardware, and important theoretical advancements, artificial intelligence has recently contributed to dramatic progress in medicine. One type of artificial intelligence known as deep learning has been particularly impactful for medical image analysis. Deep learning applications have shown promising results in dermatology and other specialties, including radiology, cardiology, and ophthalmology. The modern clinician will benefit from an understanding of the basic features of deep learning to effectively use new applications and to better gauge their utility and limitations. In this second article of a 2-part series, we review the existing and emerging clinical applications of deep learning in dermatology and discuss future opportunities and limitations. Part 1 of this series offered an introduction to the basic concepts of deep learning to facilitate effective communication between clinicians and technical experts.

Deep learning for dermatologists: Part I. Fundamental concepts.

Artificial intelligence is generating substantial interest in the field of medicine. One form of artificial intelligence, deep learning, has led to rapid advances in automated image analysis. In 2017, an algorithm demonstrated the ability to diagnose certain skin cancers from clinical photographs with the accuracy of an expert dermatologist. Subsequently, deep learning has been applied to a range of dermatology applications. Although experts will never be replaced by artificial intelligence, it will certainly affect the specialty of dermatology. In this first article of a 2-part series, the basic concepts of deep learning will be reviewed with the goal of laying the groundwork for effective communication between clinicians and technical colleagues. In part 2 of the series, the clinical applications of deep learning in dermatology will be reviewed and limitations and opportunities will be considered.

Temporal Trends, Predictors and Outcomes of Inpatient Palliative Care Use in Cardiac Arrest Complicating Acute Myocardial Infarction.

BACKGROUND: Utilization of inpatient palliative care services (PCS) has been infrequently studied in patients with cardiac arrest complicating acute myocardial infarction (AMI-CA). METHODS: Adult AMI-CA admissions were identified from the National Inpatient Sample (2000-2017). Outcomes of interest included temporal trends and predictors of PCS use and in-hospital mortality, length of stay, hospitalization costs and discharge disposition in AMI-CA admissions with and without PCS use. Multivariable logistic regression and propensity matching were used to adjust for confounding. RESULTS: Among 584,263 AMI-CA admissions, 26,919 (4.6%) received inpatient PCS. From 2000 to 2017 PCS use increased from <1% to 11.5%. AMI-CA admissions that received PCS were on average older, had greater comorbidity, higher rates of cardiogenic shock, acute organ failure, lower rates of coronary angiography (48.6% vs 63.3%), percutaneous coronary intervention (37.4% vs 46.9%), and coronary artery bypass grafting (all p < 0.001). Older age, greater comorbidity burden and acute non-cardiac organ failure were predictive of PCS use. In-hospital mortality was significantly higher in the PCS cohort (multivariable logistic regression: 84.6% vs 42.9%, adjusted odds ratio 3.62 [95% CI 3.48-3.76]; propensity-matched analysis: 84.7% vs. 66.2%, p < 0.001). The PCS cohort received a do- not-resuscitate status more often (47.6% vs. 3.7%), had shorter hospital stays (4 vs 5 days), and were discharged more frequently to skilled nursing facilities (73.6% vs. 20.4%); all p < 0.001. These results were consistent in the propensity-matched analysis. CONCLUSIONS: Despite an increase in PCS use in AMI-CA, it remains significantly underutilized highlighting the role for further integrating of these specialists in AMI-CA care.

Artificial Intelligence-Enhanced Electrocardiogram for the Early Detection of Cardiac Amyloidosis.

OBJECTIVE: To develop an artificial intelligence (AI)-based tool to detect cardiac amyloidosis (CA) from a standard 12-lead electrocardiogram (ECG). METHODS: We collected 12-lead ECG data from 2541 patients with light chain or transthyretin CA seen at Mayo Clinic between 2000 and 2019. Cases were nearest neighbor matched for age and sex, with 2454 controls. A subset of 2997 (60%) cases and controls were used to train a deep neural network to predict the presence of CA with an internal validation set (n=999; 20%) and a randomly selected holdout testing set (n=999; 20%). We performed experiments using single-lead and 6-lead ECG subsets. RESULTS: The area under the receiver operating characteristic curve (AUC) was 0.91 (CI, 0.90 to 0.93), with a positive predictive value for detecting either type of CA of 0.86. By use of a cutoff probability of 0.485 determined by the Youden index, 426 (84%) of the holdout patients with CA were detected by the model. Of the patients with CA and prediagnosis electrocardiographic studies, the AI model successfully predicted the presence of CA more than 6 months before the clinical diagnosis in 59%. The best single-lead model was V5 with an AUC of 0.86 and a precision of 0.78, with other single leads performing similarly. The 6-lead (bipolar leads) model had an AUC of 0.90 and a precision of 0.85. CONCLUSION: An AI-driven ECG model effectively detects CA and may promote early diagnosis of this life-threatening disease.

Temporal Trends, Management and Outcomes of Acute Myocardial Infarction with Concomitant Respiratory Infections.

There are limited contemporary data on the management and outcomes of acute myocardial infarction (AMI) in patients with concomitant acute respiratory infections. Hence, using the National Inpatient Sample from 2000-2017, adult AMI admissions with and without concomitant respiratory infections were identified. We evaluated in-hospital mortality, utilization of cardiac procedures, hospital length of stay, hospitalization costs, and discharge disposition. Among 10,880,856 AMI admissions, respiratory infections were identified in 745,536 (6.9%). Temporal trends revealed a relatively stable tr end with a peak during 2008-2009. Admissions with respiratory infections were on average older (74 vs. 67 years), female (45% vs 39%), with greater comorbidity (mean Charlson comorbidity index 5.9 ± 2.2 vs 4.4 ± 2.3), and had higher rates of non-ST-segment-elevation AMI presentation (71.8% vs. 62.2%) (all p < 0.001). Higher rates of cardiac arrest (8.2% vs 4.8%), cardiogenic shock (10.7% vs 4.4%), and acute organ failure (27.8% vs 8.1%) were seen in AMI admissions with respiratory infections. Coronary angiography (41.4% vs 65.6%, p < 0.001) and percutaneous coronary intervention (20.7% vs 43.5%, p < 0.001) were used less commonly in those with respiratory infections. Admissions with respiratory infections had higher in-hospital mortality (14.5% vs 5.5%; propensity matched analysis: 14.6% vs 12.5%; adjusted odds ratio 1.25 [95% confidence interval 1.24-1.26], p < 0.001), longer hospital stay, higher hospitalization costs, and less frequent discharges to home compared to those without respiratory infections. In conclusion, respiratory infections significantly impact AMI admissions with higher rates of complications, mortality and resource utilization.

Electrocardiogram screening for aortic valve stenosis using artificial intelligence.

AIMS: Early detection of aortic stenosis (AS) is becoming increasingly important with a better outcome after aortic valve replacement in asymptomatic severe AS patients and a poor outcome in moderate AS. We aimed to develop artificial intelligence-enabled electrocardiogram (AI-ECG) using a convolutional neural network to identify patients with moderate to severe AS. METHODS AND RESULTS: Between 1989 and 2019, 258 607 adults [mean age 63 ± 16.3 years; women 122 790 (48%)] with an echocardiography and an ECG performed within 180 days were identified from the Mayo Clinic database. Moderate to severe AS by echocardiography was present in 9723 (3.7%) patients. Artificial intelligence training was performed in 129 788 (50%), validation in 25 893 (10%), and testing in 102 926 (40%) randomly selected subjects. In the test group, the AI-ECG labelled 3833 (3.7%) patients as positive with the area under the curve (AUC) of 0.85. The sensitivity, specificity, and accuracy were 78%, 74%, and 74%, respectively. The sensitivity increased and the specificity decreased as age increased. Women had lower sensitivity but higher specificity compared with men at any age groups. The model performance increased when age and sex were added to the model (AUC 0.87), which further increased to 0.90 in patients without hypertension. Patients with false-positive AI-ECGs had twice the risk for developing moderate or severe AS in 15 years compared with true negative AI-ECGs (hazard ratio 2.18, 95% confidence interval 1.90-2.50). CONCLUSION: An AI-ECG can identify patients with moderate or severe AS and may serve as a powerful screening tool for AS in the community.

Right Ventricular Pulmonary Artery Coupling and Mortality in Cardiac Intensive Care Unit Patients.

Background Impaired right ventricular (RV) pulmonary artery coupling has been associated with higher mortality in patients with chronic heart disease, but few studies have examined this metric in critically ill patients. We sought to evaluate the association between RV pulmonary artery coupling, defined by the ratio of tricuspid annular peak systolic tissue Doppler velocity (TASV)/estimated RV systolic pressure (RVSP), and mortality in cardiac intensive care unit patients. Methods and Results Using a database of unique cardiac intensive care unit admissions from 2007 to 2018, we included patients with TASV/RVSP ratio measured within 1 day of hospitalization. Hospital mortality was analyzed using multivariable logistic regression, and 1-year mortality was analyzed using multivariable Cox proportional-hazards analysis. We included 4259 patients with a mean age of 69±15 years (40.1% women). Admission diagnoses included acute coronary syndrome in 56%, heart failure in 52%, respiratory failure in 24%, and cardiogenic shock in 12%. The mean TASV/RVSP ratio was 0.31±0.14, and in-hospital mortality occurred in 7% of patients. Higher TASV/RVSP ratio was associated with lower in-hospital mortality (adjusted unit odds ratio, 0.68 per each 0.1-unit higher ratio; 95% CI, 0.58-0.79; P<0.001) and lower 1-year mortality among hospital survivors (adjusted unit hazard ratio, 0.83 per each 0.1-unit higher ratio; 95% CI, 0.77-0.90; P<0.001). Stepwise decreases in hospital and 1-year mortality were observed in each higher TASV/RVSP quintile. The TASV/RVSP ratio remained associated with mortality after adjusting for left ventricular systolic and diastolic function. Conclusions A low TASV/RVSP ratio is associated with increased short-term and long-term mortality among cardiac intensive care unit patients, emphasizing importance of impaired RV pulmonary artery coupling as a determinant of poor prognosis. Further study is required to determine whether interventions to optimize RV pulmonary artery coupling can improve outcomes.

Fibrinolysis vs. primary percutaneous coronary intervention for ST-segment elevation myocardial infarction cardiogenic shock.

AIMS: There are limited contemporary data on the use of initial fibrinolysis in ST-segment elevation myocardial infarction cardiogenic shock (STEMI-CS). This study sought to compare the outcomes of STEMI-CS receiving initial fibrinolysis vs. primary percutaneous coronary intervention (PPCI). METHODS: Using the National (Nationwide) Inpatient Sample from 2009 to 2017, a comparative effectiveness study of adult (>18 years) STEMI-CS admissions receiving pre-hospital/in-hospital fibrinolysis were compared with those receiving PPCI. Admissions with alternate indications for fibrinolysis and STEMI-CS managed medically or with surgical revascularization (without fibrinolysis) were excluded. Outcomes of interest included in-hospital mortality, development of non-cardiac organ failure, complications, hospital length of stay, hospitalization costs, use of palliative care, and do-not-resuscitate status. RESULTS: During 2009-2017, 5297 and 110 452 admissions received initial fibrinolysis and PPCI, respectively. Compared with those receiving PPCI, the fibrinolysis group was more often non-White, with lower co-morbidity, and admitted on weekends and to small rural hospitals (all P < 0.001). In the fibrinolysis group, 95.3%, 77.4%, and 15.7% received angiography, PCI, and coronary artery bypass grafting, respectively. The fibrinolysis group had higher rates of haemorrhagic complications (13.5% vs. 9.9%; P < 0.001). The fibrinolysis group had comparable all-cause in-hospital mortality [logistic regression analysis: 28.8% vs. 28.5%; propensity-matched analysis: 30.8% vs. 30.3%; adjusted odds ratio 0.97 (95% confidence interval 0.90-1.05); P = 0.50]. The fibrinolysis group had comparable rates of acute organ failure, hospital length of stay, rates of palliative care referrals, do-not-resuscitate status use, and lesser hospitalization costs. CONCLUSIONS: The use of initial fibrinolysis had comparable in-hospital mortality than those receiving PPCI in STEMI-CS in the contemporary era in this large national observational study.

Improving the delivery of palliative care through predictive modeling and healthcare informatics.

OBJECTIVE: Access to palliative care (PC) is important for many patients with uncontrolled symptom burden from serious or complex illness. However, many patients who could benefit from PC do not receive it early enough or at all. We sought to address this problem by building a predictive model into a comprehensive clinical framework with the aims to (i) identify in-hospital patients likely to benefit from a PC consult, and (ii) intervene on such patients by contacting their care team. MATERIALS AND METHODS: Electronic health record data for 68 349 inpatient encounters in 2017 at a large hospital were used to train a model to predict the need for PC consult. This model was published as a web service, connected to institutional data pipelines, and consumed by a downstream display application monitored by the PC team. For those patients that the PC team deems appropriate, a team member then contacts the patient's corresponding care team. RESULTS: Training performance AUC based on a 20% holdout validation set was 0.90. The most influential variables were previous palliative care, hospital unit, Albumin, Troponin, and metastatic cancer. The model has been successfully integrated into the clinical workflow making real-time predictions on hundreds of patients per day. The model had an "in-production" AUC of 0.91. A clinical trial is currently underway to assess the effect on clinical outcomes. CONCLUSIONS: A machine learning model can effectively predict the need for an inpatient PC consult and has been successfully integrated into practice to refer new patients to PC.