Multi-omic molecular profiling guide's efficacious treatment selection in refractory metastatic breast cancer: a prospective phase II clinical trial.

This prospective phase II clinical trial (Side Out 2) explored the clinical benefits of treatment selection informed by multi-omic molecular profiling (MoMP) in refractory metastatic breast cancers (MBCs). Core needle biopsies were collected from 32 patients with MBC at trial enrollment. Patients had received an average of 3.94 previous lines of treatment in the metastatic setting before enrollment in this study. Samples underwent MoMP, including exome sequencing, RNA sequencing (RNA-Seq), immunohistochemistry, and quantitative protein pathway activation mapping by Reverse Phase Protein Microarray (RPPA). Clinical benefit was assessed using the previously published growth modulation index (GMI) under the hypothesis that MoMP-selected therapy would warrant further investigation for GMI ≥ 1.3 in ≥ 35% of the patients. Of the 32 patients enrolled, 29 received treatment based on their MoMP and 25 met the follow-up criteria established by the trial protocol. Molecular information was delivered to the tumor board in a median time frame of 14 days (11-22 days), and targetable alterations for commercially available agents were found in 23/25 patients (92%). Of the 25 patients, 14 (56%) reached GMI ≥ 1.3. A high level of DNA topoisomerase I (TOPO1) led to the selection of irinotecan-based treatments in 48% (12/25) of the patients. A pooled analysis suggested clinical benefit in patients with high TOPO1 expression receiving irinotecan-based regimens (GMI ≥ 1.3 in 66.7% of cases). These results confirmed previous observations that MoMP increases the frequency of identifiable actionable alterations (92% of patients). The MoMP proposed allows the identification of biomarkers that are frequently expressed in MBCs and the evaluation of their role as predictors of response to commercially available agents. Lastly, this study confirmed the role of MoMP for informing treatment selection in refractory MBC patients: more than half of the enrolled patients reached a GMI ≥ 1.3 even after multiple lines of previous therapies for metastatic disease.

Impact of Hospice and Palliative Care Service Utilization on All-Cause 30-Day Readmission Rate for Older Adults Hospitalized with Heart Failure.

BACKGROUND: Acute decompensated heart failure (HF) is the leading cause for hospital readmission. Large-scale sustainable interventions to reduce readmission rate have not been fully explored or proven effective. OBJECTIVE: We studied the impact of hospice and palliative care service utilization on 30-day all-cause hospital readmissions for patients with HF. METHODS AND RESULTS: Data were retrieved from the Department of Veterans Affairs Corporate Data Warehouse. The study included 238 116 HF admissions with primary diagnosis of HF belonging to 130 812 patients. Among these patients, 2592 had hospice and palliative care utilizations and 68 245 patients did not. Rehospitalization was calculated within 30 days of index hospitalization. Propensity scores were used to match hospice and nonhospice patients on demographics, Charlson comorbidity categories, and 30-day survival. In the matched group, logistic regression was used to estimate effects of hospice on readmission, controlling for any covariates that had failed to balance. The average age of the matched patients was 74 years old, 14% were African American, 75% Caucasian, 2% Asian, and 17% female. After propensity matching, the odds ratio for readmission was 1.29. The 95% confidence interval for the odds was 1.13 to 1.48, suggesting that veterans receiving services have a higher chance of readmission. CONCLUSION: In a large cohort study of older US Veterans, utilization of hospice and palliative care services was associated with a higher 30-day all-cause readmission rate among hospitalized patients with HF. Further prospective studies should be conducted to confirm results and test generalizability outside the Veterans Affairs system of care.

Predicting activities of daily living for cancer patients using an ontology-guided machine learning methodology.

BACKGROUND: Bio-ontologies are becoming increasingly important in knowledge representation and in the machine learning (ML) fields. This paper presents a ML approach that incorporates bio-ontologies and its application to the SEER-MHOS dataset to discover patterns of patient characteristics that impact the ability to perform activities of daily living (ADLs). Bio-ontologies are used to provide computable knowledge for ML methods to "understand" biomedical data. RESULTS: This retrospective study included 723 cancer patients from the SEER-MHOS dataset. Two ML methods were applied to create predictive models for ADL disabilities for the first year after a patient's cancer diagnosis. The first method is a standard rule learning algorithm; the second is that same algorithm additionally equipped with methods for reasoning with ontologies. The models showed that a patient's race, ethnicity, smoking preference, treatment plan and tumor characteristics including histology, staging, cancer site, and morphology were predictors for ADL performance levels one year after cancer diagnosis. The ontology-guided ML method was more accurate at predicting ADL performance levels (P < 0.1) than methods without ontologies. CONCLUSIONS: This study demonstrated that bio-ontologies can be harnessed to provide medical knowledge for ML algorithms. The presented method demonstrates that encoding specific types of hierarchical relationships to guide rule learning is possible, and can be extended to other types of semantic relationships present in biomedical ontologies. The ontology-guided ML method achieved better performance than the method without ontologies. The presented method can also be used to promote the effectiveness and efficiency of ML in healthcare, in which use of background knowledge and consistency with existing clinical expertise is critical.

Designing highly reliable adverse-event detection systems to predict subsequent claims.

BACKGROUND: To respond proactively to patient safety events, many healthcare organizations have been enhancing and customizing their event reporting systems. Yet an indiscriminate expansion of the range and number of event reports may reduce, rather than raise, risk managers' ability to detect events that warrant a response. To avoid becoming overwhelmed by too many event reports that have little immediate operational value, risk managers therefore require a concurrent and complementary refinement of their data-processing capabilities. OBJECTIVE: To examine the extent to which adverse event reports can predict subsequent claims. DATA AND METHODS: The study sample included all adverse event reports and all records of closed claims that related to patient care episodes between July 1, 2006, and May 31, 2009, at a large hospital system in northern Virginia. After matching closed claims to event reports, we fitted multivariate predictive models to identify event report entries that predict future claims. RESULTS: During the period under study, 20 151 event reports and 94 claims were filed across the health system. We were able to match 60 claims (63.8%) to at least 1 preceding event report, implying that only 0.3% of event reports preceded a subsequent matching claim. The superior prediction model identified 90% of eventual matched claims by retaining only 20% of all event reports. CONCLUSION: Simple prediction algorithms can supplement expert judgment by screening for reports that are likely to result in a claim, thereby enabling risk managers to evaluate adverse event reports more expeditiously and to identify, and ultimately prevent, serious safety lapses more reliably.

Diversity and distribution patterns of Pronophilina butterflies (Lepidoptera: Nymphalidae: Satyrinae) along an altitudinal transect in north-western Ecuador / Diversidad y patrones de distribución de mariposas de la subtribu Pronophilina (Lepidoptera: Nymphalidae: Satyrinae) en un transecto altitudinal en el nor-oeste de Ecuador

Se realizó en Ecuador un muestreo de mariposas de la subtribu Pronophilina con el fin de evaluar los efectos de altitud sobre los patrones de distribución, diversidad y estructura de la comunidad en un transecto altitudinal. Se demostró una correlación significativa de todos los índices de diversidad y altitud. El máximo de diversidad expresado por la riqueza de especies, fue reportado a 2600 m. Se identificaron dos grupos de especies en la parte inferior y superior del transecto. La comparación de los coeficientes de similitud indicó valores menores en la franja de altitud intermedia. Se demostró que varios pares de especies relacionadas morfo y ecológicamente tienen distribuciones altitudinales mutuamente exclusivas. La comparación con estudios semejantes revelaron una congruencia muy notable en cuanto a los patrones de diversidad altitudinal de los Pronophilina en varias áreas de la cordillera Andina. En particular, el índice de Shannon llega a valores máximos entre 2600 m y 2850 m, aproximadamente 400 - 500 por debajo del limite superior del bosque nublado. El aumento de la diversidad de los Pronophilina con la altitud puede ser relacionado tan solo de manera marginal con la mayor disponibilidad de los recursos limitados. La menor presión por parte de los depredadores, parasitoides y ectoparásitos en mayores altitudes puede contribuir a la mayor abundancia pero no directamente a la diversidad. Esta última, está relacionada con las características intrínsicas del grupo, tales como el solapamiento de grupos faunísticos elevacionales y la tasa de especiación más rápida en el límite superior del bosque.

Samplings of Pronophilina, a species-rich group of neotropical montane butterflies, were carried out along an elevational transect in Ecuador to assess the effect of altitude on their distribution patterns, diversity and community structure. All diversity indices were significantly correlated with altitude. Maximum diversity expressed in species-richness, Shannon index and Fisher alpha was recorded at 2600 m. Two assemblages of species were identified in the lower (below 2100 m) and upper (above 2300 m) sections of the transect by means of correspondence (CA) and cluster analysis. A comparison of Sørensen similarity coefficients showed lower values, thus higher turnover in the intermediate elevational band. Several closely related morphologically and ecologically species were found to have mutually exclusive altitudinal distribution patterns. A comparison with similar studies in Venezuela, Colombia and Peru revealed far reaching congruency of the patterns of altitudinal diversity of Pronophilina in distant areas of the Andes. In particular, the Shannon index reaches its maximum values at 2600-2850 m, which invariably correspond to ca. 400-500 m below the upper limit of cloud forest. Increase of diversity of Pronophilina with altitude is marginally related to higher limited resource availability. The lower pressure of predators and parasites at higher elevation can contribute with higher abundance, but cannot be directly correlated with higher diversity. Higher diversity is related with intrisic characteristics of the group, such as aggregated diversity by overlapping of elevational faunal assemblages and higher speciation ratio towards high elevations, particularly near timberline.