Development and validation of a prediction index for recent mortality in advanced COPD patients.
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ABSTRACT:
SUBMITTER: Tsai SH
PROVIDER: S-EPMC8758667 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:BackgroundConservative care (CC) may be a valid alternative to dialysis for certain older patients with advanced chronic kidney disease (CKD). A model that predicts patient prognosis on both treatment pathways could be of value in shared decision-making. Therefore, the aim is to develop a prediction tool that predicts the mortality risk for the same patient for both dialysis and CC from the time of treatment decision.MethodsCKD Stage 4/5 patients aged ≥70 years, treated at a single centre in the Netherlands, were included between 2004 and 2016. Predictors were collected at treatment decision and selected based on literature and an expert panel. Outcome was 2-year mortality. Basic and extended logistic regression models were developed for both the dialysis and CC groups. These models were internally validated with bootstrapping. Model performance was assessed with discrimination and calibration.ResultsIn total, 366 patients were included, of which 126 chose CC. Pre-selected predictors for the basic model were age, estimated glomerular filtration rate, malignancy and cardiovascular disease. Discrimination was moderate, with optimism-corrected C-statistics ranging from 0.675 to 0.750. Calibration plots showed good calibration.ConclusionsA prediction tool that predicts 2-year mortality was developed to provide older advanced CKD patients with individualized prognosis estimates for both dialysis and CC. Future studies are needed to test whether our findings hold in other CKD populations. Following external validation, this prediction tool could be used to compare a patient's prognosis on both dialysis and CC, and help to inform treatment decision-making.
| S-EPMC7857791 | biostudies-literature
Project description:BackgroundThis study describes a repeated measures prediction index to identify patients at high risk of ?grade 2 hand-foot skin reaction (HFSR) before each week of sorafenib therapy.MethodsData from 451 patients who received a sorafenib (400 mg bid) as part of a clinical trial were reviewed (Escudier B, Eisen T, Stadler WM et al. Sorafenib in advanced clear-cell renal-cell carcinoma. N Engl J Med 2007; 356: 125-134). Generalized estimating equations were used to develop the final risk model. A risk-scoring algorithm (range 0-58) was then derived from the final model coefficients. External validation was then carried out on a new sample of 1145 patients who received sorafenib under an expanded access program.ResultsPretreatment white blood cell count, female gender, good performance status, presence of lung and liver metastases and number of affected organs were predictors for ?grade 2 HFSR. A nonlinear association between HFSR risk and treatment duration was also identified where risk was maximized at week 5 followed by a gradual decline. Before each week of therapy, patients with risk scores>40 would be considered at high risk for developing ?grade 2 HFSR.ConclusionsThe application and planned continued refinement of this prediction tool will be an important source of patient-specific risk information for the development of moderate to severe HFSR.
| S-EPMC3403729 | biostudies-literature
Project description:While social characteristics are well-known predictors of mortality, prediction models rely almost exclusively on demographics, medical comorbidities, and function. Lacking an efficient way to summarize the prognostic impact of social factor, many studies exclude social factors altogether. Our objective was to develop and validate a summary measure of social risk and determine its ability to risk-stratify beyond traditional risk models. We examined participants in the Health and Retirement Study, a longitudinal, survey of US older adults. We developed the model from a comprehensive inventory of 183 social characteristics using least absolute shrinkage and selection operator, a penalized regression approach. Then, we assessed the predictive capacity of the model and its ability to improve on traditional prediction models. We studied 8,250 adults aged ≥65 y. Within 4 y of the baseline interview, 22% had died. Drawn from 183 possible predictors, the Social Frailty Index included age, gender, and eight social predictors: neighborhood cleanliness, perceived control over financial situation, meeting with children less than yearly, not working for pay, active with children, volunteering, feeling isolated, and being treated with less courtesy or respect. In the validation cohort, predicted and observed mortality were strongly correlated. Additionally, the Social Frailty Index meaningfully risk-stratified participants beyond the Charlson score (medical comorbidity index) and the Lee Index (comorbidity and function model). The Social Frailty Index includes age, gender, and eight social characteristics and accurately risk-stratifies older adults. The model improves upon commonly used risk prediction tools and has application in clinical, population health, and research settings.
| S-EPMC9963593 | biostudies-literature
Project description:BackgroundPrimary malignant cardiac tumors (PMCTs) are correlated with an unfavourable prognosis. The aim of the current study was to establish and validate a nomogram model for 3-month mortality prediction for patients with PMCT.MethodsA total of 638 PMCT patients diagnosed between 1975 to 2016 in the Surveillance, Epidemiology, and End Results (SEER) database were randomly enrolled and assigned into a training cohort (N=448) and validation cohort (N=190). Early mortality cases were analyzed, and related risk factors were identified by logistic regression models, and significant risk factors were used to establish a predictive nomogram model. The predictive capability of the model was validated by calibration analysis and receiver operating curve (ROC) in both training and validation cohorts.ResultsMultivariate logistic analysis revealed the independent risk factors for early mortality were old age, chemotherapy, surgery, and tumor stage, and these were used to construct the nomogram. In terms of calibration and discrimination, both the internal and external validation calibration curves revealed consistency between the nomogram prediction and the actual observation. The area under the curve (AUC) of the nomogram for 3-month mortality in the internal and external validation was 0.816 and 0.805, respectively.ConclusionsOld age and advanced tumor stage are involved in higher odds of early mortality, while surgery and chemotherapy could reduce this. The nomogram model provides an accurate, user-friendly, and reproducible tool for predicting early mortality in PMCT patients.
| S-EPMC8667126 | biostudies-literature
Project description:IntroductionOlder patients frequently present to the emergency department (ED) with nonspecific complaints (NSC), such as generalized weakness. They are at risk of adverse outcomes, and early risk stratification is crucial. Triage using Emergency Severity Index (ESI) is reliable and valid, but older patients are prone to undertriage, most often at decision point D. The aim of this study was to assess the predictive power of additional clinical parameters in NSC patients.MethodsBaseline demographics, vital signs, and deterioration of activity of daily living (ADL) in patients with NSC were prospectively assessed at four EDs. Physicians scored the coherence of history and their first impression. For prediction of 30-day mortality, we combined vital signs at decision point D (heart rate, respiratory rate, oxygen saturation) as "ESI vital," and added "ADL deterioration," "incoherence of history," or "first impression," using logistic regression models.ResultsWe included 948 patients with a median age of 81 years, 62% of whom were female. The baseline parameters at decision point D (ESI vital) showed an area under the curve (AUC) of 0.64 for predicting 30-day mortality in NSC patients. AUCs increased to 0.67 by adding ADL deterioration to 0.66 by adding incoherence of history, and to 0.71 by adding first impression. Maximal AUC was 0.73, combining all parameters.ConclusionAdding the physicians' first impressions to vital signs at decision point D increases predictive power of 30-day mortality significantly. Therefore, a modified ESI could improve predictive power of triage in older patients presenting with NSCs.
| S-EPMC6625680 | biostudies-literature
Project description:BackgroundTo develop, validate, and assess the clinical impact of a clinical score to predict a 6-month mortality risk among older cancer patients.ResultsThe mean age was 81.2 ± 6.1 years (women: 54%, various cancers, metastatic cancer: 45%). The score, namely the GRADE, included two geriatric variables (unintentional weight loss, impaired mobility), two oncological variables (cancer site, cancer extension), and exclusively supportive care. Up to a 14% risk of early death, the decision curves suggest that cancer treatment should be instated.ConclusionWe have developed and validated a simple score, easy to implement in daily oncological practice, to predict early death among older cancer patients which could guide oncologists in their treatment decisions.Methods603 outpatients prospectively included in the Physical Frailty in Elderly Cancer patients cohort study. We created a multivariate prediction model by evaluating the strength of the individual components of the Geriatric Assessment regarding risk of death at 6 months. Each component was evaluated by univariate analysis and the significant variables (P ≤ 0.20) were carried on as covariates in the multivariate cox proportion hazard analysis. The beta coefficients from the model were used to build a point-based scoring system. Clinical impact was assessed using decision curves.
| S-EPMC7093177 | biostudies-literature
Project description:BackgroundThe existing literature does not provide a prediction model for mortality of all colorectal cancer patients using contemporary national hospital data. We developed and validated such a model to predict colorectal cancer death within 90, 180 and 365 days after diagnosis.MethodsCohort study using linked national cancer and death records. The development population included 27,480 patients diagnosed in England in 2015. The test populations were diagnosed in England in 2016 (n = 26,411) and Wales in 2015-2016 (n = 3814). Predictors were age, gender, socioeconomic status, referral source, performance status, tumour site, TNM stage and treatment intent. Cox regression models were assessed using Brier scores, c-indices and calibration plots.ResultsIn the development population, 7.4, 11.7 and 17.9% of patients died from colorectal cancer within 90, 180 and 365 days after diagnosis. T4 versus T1 tumour stage had the largest adjusted association with the outcome (HR 4.67; 95% CI: 3.59-6.09). C-indices were 0.873-0.890 (England) and 0.856-0.873 (Wales) in the test populations, indicating excellent separation of predicted risks by outcome status. Models were generally well calibrated.ConclusionsThe model was valid for predicting short-term colorectal cancer mortality. It can provide personalised information to support clinical practice and research.
| S-EPMC7652941 | biostudies-literature
Project description:BackgroundEarly prediction of hospital mortality is crucial for ICU patients with sepsis. This study aimed to develop a novel blending machine learning (ML) model for hospital mortality prediction in ICU patients with sepsis.MethodsTwo ICU databases were employed: eICU Collaborative Research Database (eICU-CRD) and Medical Information Mart for Intensive Care III (MIMIC-III). All adult patients who fulfilled Sepsis-3 criteria were identified. Samples from eICU-CRD constituted training set and samples from MIMIC-III constituted test set. Stepwise logistic regression model was used for predictor selection. Blending ML model which integrated nine sorts of basic ML models was developed for hospital mortality prediction in ICU patients with sepsis. Model performance was evaluated by various measures related to discrimination or calibration.ResultsTwelve thousand five hundred fifty-eight patients from eICU-CRD were included as the training set, and 12,095 patients from MIMIC-III were included as the test set. Both the training set and the test set showed a hospital mortality of 17.9%. Maximum and minimum lactate, maximum and minimum albumin, minimum PaO2/FiO2 and age were important predictors identified by both random forest and extreme gradient boosting algorithm. Blending ML models based on corresponding set of predictors presented better discrimination than SAPS II (AUROC, 0.806 vs. 0.771; AUPRC 0.515 vs. 0.429) and SOFA (AUROC, 0.742 vs. 0.706; AUPRC 0.428 vs. 0.381) on the test set. In addition, calibration curves showed that blending ML models had better calibration than SAPS II.ConclusionsThe blending ML model is capable of integrating different sorts of basic ML models efficiently, and outperforms conventional severity scores in predicting hospital mortality among septic patients in ICU.
| S-EPMC8365981 | biostudies-literature
Project description:Validation dataset of 298 ER-positive patients treated with tamoxifen for 5 years. All patients in this dataset have ER+ breast cancer and were uniformly treated with tamoxifen for 5 years. The objective of the study was to correlate levels of genomic markers to outcomes (relapse free survival) in this cohort of uniformly treated patients.
2010-08-13 | GSE17705 | GEO
Project description:BACKGROUND:Early identification of noninvasive ventilation (NIV) failure is a promising strategy for reducing mortality in chronic obstructive pulmonary disease (COPD) patients. However, a risk-scoring system is lacking. METHODS:To develop a scale to predict NIV failure, 500 COPD patients were enrolled in a derivation cohort. Heart rate, acidosis (assessed by pH), consciousness (assessed by Glasgow coma score), oxygenation, and respiratory rate (HACOR) were entered into the scoring system. Another two groups of 323 and 395 patients were enrolled to internally and externally validate the scale, respectively. NIV failure was defined as intubation or death during NIV. RESULTS:Using HACOR score collected at 1-2 h of NIV to predict NIV failure, the area under the receiver operating characteristic curves (AUC) was 0.90, 0.89, and 0.71 for the derivation, internal-validation, and external-validation cohorts, respectively. For the prediction of early NIV failure in these three cohorts, the AUC was 0.91, 0.96, and 0.83, respectively. In all patients with HACOR score >?5, the NIV failure rate was 50.2%. In these patients, early intubation (<?48 h) was associated with decreased hospital mortality (unadjusted odds ratio?=?0.15, 95% confidence interval 0.05-0.39, p?<?0.01). CONCLUSIONS:HACOR scores exhibited good predictive power for NIV failure in COPD patients, particularly for the prediction of early NIV failure (<?48 h). In high-risk patients, early intubation was associated with decreased hospital mortality.
| S-EPMC6766459 | biostudies-literature