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Predicting neutropenia risk in patients with cancer using electronic data.

ABSTRACT: Objectives:Clinical guidelines recommending the use of myeloid growth factors are largely based on the prescribed chemotherapy regimen. The guidelines suggest that oncologists consider patient-specific characteristics when prescribing granulocyte-colony stimulating factor (G-CSF) prophylaxis; however, a mechanism to quantify individual patient risk is lacking. Readily available electronic health record (EHR) data can provide patient-specific information needed for individualized neutropenia risk estimation. An evidence-based, individualized neutropenia risk estimation algorithm has been developed. This study evaluated the automated extraction of EHR chemotherapy treatment data and externally validated the neutropenia risk prediction model. Materials and Methods:A retrospective cohort of adult patients with newly diagnosed breast, colorectal, lung, lymphoid, or ovarian cancer who received the first cycle of a cytotoxic chemotherapy regimen from 2008 to 2013 were recruited from a single cancer clinic. Electronically extracted EHR chemotherapy treatment data were validated by chart review. Neutropenia risk stratification was conducted and risk model performance was assessed using calibration and discrimination. Results:Chemotherapy treatment data electronically extracted from the EHR were verified by chart review. The neutropenia risk prediction tool classified 126 patients (57%) as being low risk for febrile neutropenia, 44 (20%) as intermediate risk, and 51 (23%) as high risk. The model was well calibrated (Hosmer-Lemeshow goodness-of-fit test?=?0.24). Discrimination was adequate and slightly less than in the original internal validation (c-statistic 0.75 vs 0.81). Conclusion:Chemotherapy treatment data were electronically extracted from the EHR successfully. The individualized neutropenia risk prediction model performed well in our retrospective external cohort.

SUBMITTER: Pawloski PA

PROVIDER: S-EPMC6277060 | biostudies-literature | 2017 Apr

REPOSITORIES: biostudies-literature

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Predicting neutropenia risk in patients with cancer using electronic data.

Pawloski Pamala A PA Thomas Avis J AJ Kane Sheryl S Vazquez-Benitez Gabriela G Shapiro Gary R GR Lyman Gary H GH

Journal of the American Medical Informatics Association : JAMIA 20170401 e1

<h4>Objectives</h4>Clinical guidelines recommending the use of myeloid growth factors are largely based on the prescribed chemotherapy regimen. The guidelines suggest that oncologists consider patient-specific characteristics when prescribing granulocyte-colony stimulating factor (G-CSF) prophylaxis; however, a mechanism to quantify individual patient risk is lacking. Readily available electronic health record (EHR) data can provide patient-specific information needed for individualized neutrope ...[more]

PMID: 27638907

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Project description:BackgroundPatients with cancer starting systemic treatment programs, such as chemotherapy, often develop depression. A prediction model may assist physicians and health care workers in the early identification of these vulnerable patients.ObjectiveThis study aimed to develop a prediction model for depression risk within the first month of cancer treatment.MethodsWe included 16,159 patients diagnosed with cancer starting chemo- or radiotherapy treatment between 2008 and 2021. Machine learning models (eg, least absolute shrinkage and selection operator [LASSO] logistic regression) and natural language processing models (Bidirectional Encoder Representations from Transformers [BERT]) were used to develop multimodal prediction models using both electronic health record data and unstructured text (patient emails and clinician notes). Model performance was assessed in an independent test set (n=5387, 33%) using area under the receiver operating characteristic curve (AUROC), calibration curves, and decision curve analysis to assess initial clinical impact use.ResultsAmong 16,159 patients, 437 (2.7%) received a depression diagnosis within the first month of treatment. The LASSO logistic regression models based on the structured data (AUROC 0.74, 95% CI 0.71-0.78) and structured data with email classification scores (AUROC 0.74, 95% CI 0.71-0.78) had the best discriminative performance. The BERT models based on clinician notes and structured data with email classification scores had AUROCs around 0.71. The logistic regression model based on email classification scores alone performed poorly (AUROC 0.54, 95% CI 0.52-0.56), and the model based solely on clinician notes had the worst performance (AUROC 0.50, 95% CI 0.49-0.52). Calibration was good for the logistic regression models, whereas the BERT models produced overly extreme risk estimates even after recalibration. There was a small range of decision thresholds for which the best-performing model showed promising clinical effectiveness use. The risks were underestimated for female and Black patients.ConclusionsThe results demonstrated the potential and limitations of machine learning and multimodal models for predicting depression risk in patients with cancer. Future research is needed to further validate these models, refine the outcome label and predictors related to mental health, and address biases across subgroups.

Project description:BACKGROUND:Readmissions after hospitalization for pneumonia are common, but the few risk-prediction models have poor to modest predictive ability. Data routinely collected in the electronic health record (EHR) may improve prediction. OBJECTIVE:To develop pneumonia-specific readmission risk-prediction models using EHR data from the first day and from the entire hospital stay ("full stay"). DESIGN:Observational cohort study using stepwise-backward selection and cross-validation. SUBJECTS:Consecutive pneumonia hospitalizations from 6 diverse hospitals in north Texas from 2009-2010. MEASURES:All-cause nonelective 30-day readmissions, ascertained from 75 regional hospitals. RESULTS:Of 1463 patients, 13.6% were readmitted. The first-day pneumonia-specific model included sociodemographic factors, prior hospitalizations, thrombocytosis, and a modified pneumonia severity index; the full-stay model included disposition status, vital sign instabilities on discharge, and an updated pneumonia severity index calculated using values from the day of discharge as additional predictors. The full-stay pneumonia-specific model outperformed the first-day model (C statistic 0.731 vs 0.695; P = 0.02; net reclassification index = 0.08). Compared to a validated multi-condition readmission model, the Centers for Medicare and Medicaid Services pneumonia model, and 2 commonly used pneumonia severity of illness scores, the full-stay pneumonia-specific model had better discrimination (C statistic range 0.604-0.681; P < 0.01 for all comparisons), predicted a broader range of risk, and better reclassified individuals by their true risk (net reclassification index range, 0.09-0.18). CONCLUSIONS:EHR data collected from the entire hospitalization can accurately predict readmission risk among patients hospitalized for pneumonia. This approach outperforms a first-day pneumonia-specific model, the Centers for Medicare and Medicaid Services pneumonia model, and 2 commonly used pneumonia severity of illness scores. Journal of Hospital Medicine 2017;12:209-216.

Project description:ImportanceSecondary prevention interventions to reduce post-stroke cognitive impairment (PSCI) can be aided by the early identification of high-risk individuals who would benefit from risk factor modification.ObjectiveTo develop and evaluate a predictive model to identify patients at increased risk of PSCI over 5 years using data easily accessible from electronic health records.DesignCohort study with patients enrolled between 2003-2016 with follow-up through 2022.SettingPrimary care practices affiliated with two academic medical centers.ParticipantsIndividuals 45 years or older, without prior stroke or prevalent cognitive impairment, with primary care visits and an incident ischemic stroke between 2003-2016 (development/internal validation cohort) or 2010-2022 (external validation cohort).ExposuresPredictors of PSCI were ascertained from the electronic health record.Main outcomeThe outcome was incident dementia/cognitive impairment within 5 years and beginning 3 months following stroke, ascertained using ICD-9/10 codes. For model variable selection, we considered potential predictors of PSCI and constructed 400 bootstrap samples with two-thirds of the model derivation sample. We ran 10-fold cross-validated Cox proportional hazards models using a least absolute shrinkage and selection operator (LASSO) penalty. Variables selected in >25% of samples were included.ResultsThe analysis included 332 incident diagnoses of PSCI in the development cohort (n=3,741), and 161 and 128 incident diagnoses in the internal (n=1,925) and external (n=2,237) validation cohorts. The c-statistic for predicting PSCI was 0.731 (95% CI: 0.694-0.768) in the internal validation cohort, and 0.724 (95% CI: 0.681-0.766) in the external validation cohort. A risk score based on the beta coefficients of predictors from the development cohort stratified patients into low (0-7 points), intermediate (8-11 points), and high (12-35 points) risk groups. The hazard ratios for incident PSCI were significantly different by risk categories in internal (High, HR: 6.2, 95% CI 4.1-9.3; Intermediate, HR 2.7, 95% CI: 1.8-4.1) and external (High, HR: 6.1, 95% CI: 3.9-9.6; Intermediate, HR 2.8, 95% CI: 1.9-4.3) validation cohorts.Conclusions and relevanceFive-year risk of PSCI can be accurately predicted using routinely collected data. Model output can be used to risk stratify and identify individuals at increased risk for PSCI for preventive efforts.

Dataset Information

Predicting neutropenia risk in patients with cancer using electronic data.

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Predicting neutropenia risk in patients with cancer using electronic data.

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