Background: Patients admitted to general wards are inherently at risk of deterioration. Thus, tools that can provide early detection of deterioration may be lifesaving. Frequent remote patient monitoring (RPM) has the potential to allow such early detection, leading to a timely intervention by health care providers. Objective: This study aimed to assess the potential of a novel wearable RPM device to provide timely alerts in patients at high risk for deterioration. Methods: This prospective observational study was conducted in two general wards of a large tertiary medical center. Patients determined to be at high risk to deteriorate upon admission and assigned to a telemetry bed were included. On top of the standard monitoring equipment, a wearable monitor was attached to each patient, and monitoring was conducted in parallel. The data gathered by the wearable monitors were analyzed retrospectively, with the medical staff being blinded to them in real time. Several early warning scores of the risk for deterioration were used, all calculated from frequent data collected by the wearable RPM device: these included (1) the National Early Warning Score (NEWS), (2) Airway, Breathing, Circulation, Neurology, and Other (ABCNO) score, and (3) deterioration criteria defined by the clinical team as a “wish list” score. In all three systems, the risk scores were calculated every 5 minutes using the data frequently collected by the wearable RPM device. Data generated by the early warning scores were compared with those obtained from the clinical records of actual deterioration among these patients. Results: In total, 410 patients were recruited and 217 were included in the final analysis. The median age was 71 (IQR 62-78) years and 130 (59.9%) of them were male. Actual clinical deterioration occurred in 24 patients. The NEWS indicated high alert in 16 of these 24 (67%) patients, preceding actual clinical deterioration by 29 hours on average. The ABCNO score indicated high alert in 18 (75%) of these patients, preceding actual clinical deterioration by 38 hours on average. Early warning based on wish list scoring criteria was observed for all 24 patients 40 hours on average before clinical deterioration was detected by the medical staff. Importantly, early warning based on the wish list scoring criteria was also observed among all other patients who did not deteriorate. Conclusions: Frequent remote patient monitoring has the potential for early detection of a high risk to deteriorate among hospitalized patients, using both grouped signal-based scores and algorithm-based prediction. In this study, we show the ability to formulate scores for early warning by using RPM. Nevertheless, early warning scores compiled on the basis of these data failed to deliver reasonable specificity. Further efforts should be directed at improving the specificity and sensitivity of such tools. Trial Registration: ClinicalTrials.gov NCT04220359; https://clinicaltrials.gov/ct2/show/NCT04220359

Sarcopenia and frailty are causes for morbidity and mortality amongst heart failure (HF) patients. Low alanine transaminase (ALT) is a marker for these syndromes and, therefore, could serve as a biomarker for the prognostication of HF patients. We performed a retrospective analysis of all consecutive hospitalized HF patients in our institute in order to find out whether low ALT values would be a biomarker for poor outcomes. Our cohort included 11,102 patients, 35.6% categorized as heart failure with reduced ejection fraction. We excluded patients with ALT > 40 IU/L and cirrhosis. 8700 patients were followed for a median duration of 22 months and included in a univariate analysis. Patients with ALT < 10 IU/L were older (mean age 78.6 vs. 81.8, p < 0.001), had past stroke (24.6% vs. 19.6%, p < 0.001), dementia (7.7% vs. 4.6%, p < 0.001), and malignancy (13.4% vs. 10.2%, p = 0.003). Hospitalization length was longer in the low-ALT group (4 vs. 3 days, p < 0.001), and the rate of acute kidney injury during hospitalization was higher (19.1% vs. 15.6%; p = 0.006). The in-hospital mortality rate was higher in the low-ALT group (6.5% vs. 3.9%; p < 0.001). Long-term mortality was also higher (73.3% vs. 61.5%; p < 0.001). In a multivariate regression analysis, ALT < 10 IU/L had a 1.22 hazard ratio for mortality throughout the follow-up period (CI = 1.09–1.36; p < 0.001). Low ALT plasma level, a biomarker for sarcopenia and frailty, can assist clinicians in prognostic stratification of heart failure patients.

Background: The association of pulmonary and systemic arterial hypertension is believed to be mediated through hypertensive left heart disease. The purpose of the current study was to investigate whether pulmonary hypertension (PHT) is associated with systemic arterial hypertension among patients with apparently normal left ventricular diastolic function. Methods and Results: Consecutive patients who had echocardiographic evaluation between 2007 and 2019 were enrolled. Patients with disease states that are known to be associated with PHT, including diastolic dysfunction, were excluded from the analysis. Estimated right ventricular systolic pressure was extracted for all patients from the echocardiographic reports. PHT was defined as estimated right ventricular systolic pressure >40 mm Hg. Multivariate logistic regression models were applied. Final study population included 25 916 patients with a median age of 59 (interquartile range, 44–69) years, of whom 12 501 (48%) were men and 13 265 (51%) had systemic arterial hypertension. Compared with normotensive patients, hypertensive patients were 3.2 times more likely to have PHT (95% CI, 2.91–3.53; P <0.001). A multivariate model adjusted for clinical and echocardiographic parameters that are known to be associated with PHT demonstrated that hypertensive patients are almost 3 times more likely to have PHT (95% CI, 2.45–3.15; P <0.001). The association was significant in multiple subgroups but was more significant among women compared with men (odds ratio, 3.1 versus 2.4; P for interaction <0.001). Conclusions: PHT is associated with systemic arterial hypertension irrespective of left heart disease. The association is more pronounced among women.

Background. Sarcopenia is characterized by the loss of muscle mass and function and is associated with frailty, a syndrome linked to an increased likelihood of falls, fractures, and physical disability. Both frailty and sarcopenia are recognized as markers for shortened survival in a number of medical conditions and in cancer patient populations. Low alanine aminotransferase (ALT) values, representing low muscle mass (sarcopenia), may be associated with increased frailty and subsequently shortened survival in cancer patients. In the current study, we aimed to assess the potential relationship between low ALT and shorter survival in bladder cancer patients and survivors. Patients and Methods. This was a retrospective analysis of bladder cancer patients and survivors, both in and outpatients. We defined patients with sarcopenia as those presenting with ALT < 17 IU/L. Results. A total of 5769 bladder cancer patients’ records were identified. After the exclusion of patients with no available ALT values or ALT levels above the upper normal limit, the final study cohort included 3075 patients (mean age 73.2 ± 12 years), of whom 80% were men and 1362 (53% had ALT ≤ 17 IU/L. The mean ALT value of patients within the low ALT group was 11.44 IU/L, while the mean value in the higher ALT level group was 24.32 IU/L (p < 0.001). Patients in the lower ALT group were older (74.7 vs. 71.4 years; p < 0.001), had lower BMI (25.8 vs. 27; p < 0.001), and their hemoglobin values were lower (11.7 vs. 12.6 g/dL; p < 0.001). In a univariate analysis, low ALT levels were associated with a 45% increase in mortality (95% CI 1.31–1.60, p < 0.001). In a multivariate model controlling for age, kidney function, and hemoglobin, low ALT levels were still associated with 22% increased mortality. Conclusions. Low ALT values, indicative of sarcopenia and frailty, are associated with decreased survival of bladder cancer patients and survivors and could potentially be applied for optimizing individual treatment decisions.

Background: Under- or late identification of pulmonary embolism (PE)—a thrombosis of 1 or more pulmonary arteries that seriously threatens patients’ lives—is a major challenge confronting modern medicine. Objective: We aimed to establish accurate and informative machine learning (ML) models to identify patients at high risk for PE as they are admitted to the hospital, before their initial clinical checkup, by using only the information in their medical records. Methods: We collected demographics, comorbidities, and medications data for 2568 patients with PE and 52,598 control patients. We focused on data available prior to emergency department admission, as these are the most universally accessible data. We trained an ML random forest algorithm to detect PE at the earliest possible time during a patient’s hospitalization—at the time of his or her admission. We developed and applied 2 ML-based methods specifically to address the data imbalance between PE and non-PE patients, which causes misdiagnosis of PE. Results: The resulting models predicted PE based on age, sex, BMI, past clinical PE events, chronic lung disease, past thrombotic events, and usage of anticoagulants, obtaining an 80% geometric mean value for the PE and non-PE classification accuracies. Although on hospital admission only 4% (1942/46,639) of the patients had a diagnosis of PE, we identified 2 clustering schemes comprising subgroups with more than 61% (705/1120 in clustering scheme 1; 427/701 and 340/549 in clustering scheme 2) positive patients for PE. One subgroup in the first clustering scheme included 36% (705/1942) of all patients with PE who were characterized by a definite past PE diagnosis, a 6-fold higher prevalence of deep vein thrombosis, and a 3-fold higher prevalence of pneumonia, compared with patients of the other subgroups in this scheme. In the second clustering scheme, 2 subgroups (1 of only men and 1 of only women) included patients who all had a past PE diagnosis and a relatively high prevalence of pneumonia, and a third subgroup included only those patients with a past diagnosis of pneumonia. Conclusions: This study established an ML tool for early diagnosis of PE almost immediately upon hospital admission. Despite the highly imbalanced scenario undermining accurate PE prediction and using information available only from the patient’s medical history, our models were both accurate and informative, enabling the identification of patients already at high risk for PE upon hospital admission, even before the initial clinical checkup was performed. The fact that we did not restrict our patients to those at high risk for PE according to previously published scales (eg, Wells or revised Genova scores) enabled us to accurately assess the application of ML on raw medical data and identify new, previously unidentified risk factors for PE, such as previous pulmonary disease, in general populations.