Portal use has been studied among outpatients, but its utility and impact on inpatients is unclear. This study describes portal adoption and use among hospitalized cancer patients and investigates associations with selected safety, utilization, and satisfaction measures. A retrospective review of 4594 adult hospitalized cancer patients was conducted between 2012 and 2014 at Mayo Clinic in Jacksonville, Florida, comparing portal adopters, who registered for a portal account prior to hospitalization, with nonadopters. Adopters were classified by their portal activity during hospitalization as active or inactive inpatient users. Univariate and several logistic and linear regression models were used for analysis. Of total patients, 2352 (51.2%) were portal adopters, and of them, 632 (26.8%) were active inpatient users. Portal adoption was associated with patients who were young, female, married, with higher income, and had more frequent hospitalizations (P < .05). Active inpatient use was associated with patients who were young, married, nonlocals, with higher disease severity, and were hospitalized for medical treatment (P < .05). In univariate analyses, self-management knowledge scores were higher among adopters vs nonadopters (84.3 and 80.0, respectively; P = .01) and among active vs inactive inpatient users (87.0 and 83.3, respectively; P = .04). In regression models adjusted for age and disease severity, the association between portal behaviors and majority of measures were not significant (P > .05). Over half of our cancer inpatients adopted a portal prior to hospitalization, with increased adoption associated with predisposing and enabling determinants (eg: age, sex, marital status, income), and increased inpatient use associated with need (eg: nonlocal residence and disease severity). Additional research and greater effort to expand the portal functionality is needed to impact inpatient outcomes.

To determine whether use of a patient portal during hospitalization is associated with improvement in hospital outcomes, 30-day readmissions, inpatient mortality, and 30-day mortality. We performed a retrospective propensity score–matched study that included all adult patients admitted to Mayo Clinic Hospital in Jacksonville, Florida, from August 1, 2012, to July 31, 2014, who had signed up for a patient portal account prior to hospitalization (N = 7538). Out of the admitted patients with a portal account, 1566 (20.8%) accessed the portal while in the hospital. Compared to patients who did not access the portal, patients who accessed the portal were younger (58.8 years vs 62.3 years), had fewer elective admissions (54.2% vs 64.1%), were more frequently admitted to medical services (45.8% vs 35.2%), and were more likely to have liver disease (21.9% vs 12.9%) and higher disease severity scores (0.653 vs 0.456). After propensity score matching, there was no statistically significant difference between the 2 cohorts with respect to 30-day readmission (P = .13), inpatient mortality (P = .82), or 30-day mortality (P = .082). Use of the patient portal in the inpatient setting may not improve hospital outcomes. Future research should examine the association of portal use with more immediate inpatient health outcomes such as patient experience, patient engagement, medication reconciliation, and prevention of adverse events.

Background: Cardiogenic shock (CS) is a life-threatening complication of ST-elevation myocardial infarction (STEMI) and remains the leading cause of in-hospital mortality, with rates ranging from 5 to 10% despite advances in reperfusion strategies. Early identification and timely intervention are critical for improving outcomes. This study investigates the utility of machine learning (ML) models for predicting the risk of CS during the early phases of care—prehospital, emergency department (ED), and cardiology-on-call—with a focus on accurate triage and prioritization for urgent angiography. Results: In the prehospital phase, the Extra Trees classifier demonstrated the highest overall performance. It achieved an accuracy (ACC) of 0.9062, precision of 0.9078, recall of 0.9062, F1-score of 0.9061, and Matthews correlation coefficient (MCC) of 0.8140, indicating both high predictive power and strong generalization. In the ED phase, the support vector machine model outperformed others with an ACC of 78.12%. During the cardiology-on-call phase, Random Forest showed the best performance with an ACC of 81.25% and consistent values across other metrics. Quadratic discriminant analysis showed consistent and generalizable performance across all early care stages. Key predictive features included the Killip class, ECG rhythm, creatinine, potassium, and markers of renal dysfunction—parameters readily available in routine emergency settings. The greatest clinical utility was observed in prehospital and ED phases, where ML models could support the early identification of critically ill patients and could prioritize coronary catheterization, especially important for centers with limited capacity for angiography. Conclusions: Machine learning-based predictive models offer a valuable tool for early risk stratification in STEMI patients at risk for cardiogenic shock. These findings support the implementation of ML-driven tools in early STEMI care pathways, potentially improving survival through faster and more accurate decision-making, especially in time-sensitive clinical environments.

To present the clinical characteristics and outcome of transplant and nontransplant patients with invasive nocardiosis. We conducted a retrospective chart review of 110 patients 18 years and older diagnosed with culture-proven invasive nocardiosis (defined as the presence of clinical signs and/or radiographic abnormalities) between August 1, 1998, and November 30, 2018. Information on demographic, clinical, radiographic, and microbiological characteristics as well as mortality was collected. One hundred ten individuals with invasive nocardiosis were identified, of whom 54 (49%) were transplant and 56 nontransplant (51%) patients. Most transplant patients were kidney and lung recipients. The overall mean age was 64.9 years, and transplant patients had a higher prevalence of diabetes and chronic kidney disease. A substantial proportion of nontransplant patients were receiving corticosteroids (39%), immunosuppressive medications (16%), and chemotherapy (9%) and had chronic obstructive pulmonary disease (20%), rheumatologic conditions (18%), and malignant neoplasia (18%). A higher proportion of transplant patients (28%) than nontransplant patients (4%) received trimethoprim-sulfamethoxazole prophylaxis. In both groups, the lung was the most common site of infection. Seventy percent of all Nocardia species isolated were present in almost equal proportion: N brasiliensis (16%), N farcinica (16%), N nova (15%), N cyriacigeorgia (13%), and N asteroides (11%). More than 90% of isolates were susceptible to trimethoprim-sulfamethoxazole, linezolid, and amikacin. There was no significant difference in mortality between the 2 groups at 1, 6, and 12 months after the initial diagnosis. The frequency of invasive Nocardia infection was similar in transplant and nontransplant patients and mortality at 1, 6, and 12 months was similar in both groups. Trimethoprim-sulfamethoxazole prophylaxis failed to prevent Nocardia infection.

To report ten-year experience with a parathyroid hormone (PTH)-guided Calcium (Ca) and calcitriol supplementation protocol in reducing the incidence of hypocalcemia after total thyroidectomy (TT). Single-institution, retrospective chart review of patients who underwent TT. One hundred forty-eight patients had an operation prior to the protocol introduction and 735 had surgery after the protocol was started. Total serum Ca < 8 mg/dL was defined as hypocalcemia. The 4h-PTH stratified patients into low (>30 pg/mL), intermediate (15-30 pg/mL), and high (<15 pg/mL) hypocalcemia risk groups, to guide Ca and calcitriol management. Demographic information, pre- and post-operative characteristics, and outcomes were recorded. Fisher's exact test and Wilcoxon rank sum test were used to compare the characteristics between the 2 groups. Multivariable logistic regression was used to account for confounding variables. Area under the ROC curve was used to determine optimal 4h-PTH value as predictor of hypocalcemia. The post-protocol group had significantly lower hypocalcemia incidence (9.9% vs. 20.9%, p<0.001) and related readmissions (0.9% vs. 4.7%, p=0.004) than the pre-protocol group. Lymph node dissection and parathyroids embedded in thyroid tissue were significant risk factors for hypocalcemia. Hypocalcemia occurred in 24.3% of patients with PTH 30pg/mL. Lymph node dissection and parathyroids embedded in thyroid tissue were significant risk factors. . A PTH-guided Ca and calcitriol supplementation protocol significantly reduces post-TT hypocalcemia and related readmissions and may represent a useful approach in managing suitable candidates for outpatient TT.