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1.
J Clin Epidemiol ; : 111464, 2024 Jul 15.
Artículo en Inglés | MEDLINE | ID: mdl-39019349

RESUMEN

BACKGROUND: Cardiovascular disease (CVD) risk scores provide point estimates of individual risk without uncertainty quantification. The objective of the current study was to demonstrate the feasibility and clinical utility of calculating uncertainty surrounding individual CVD-risk predictions using Bayesian methods. METHODS AND RESULTS: Individuals with established atherosclerotic CVD were included from the Utrecht Cardiovascular Cohort - Secondary Manifestations of ARTerial disease (UCC-SMART). In 8,355 individuals, followed for median of 8.2 years (IQR 4.2-12.5), a Bayesian Weibull model was derived to predict the 10-year risk of recurrent CVD events. Model coefficients and individual predictions were very similar to that of a traditional ('frequentist') model but the Bayesian model also predicted 95% credible intervals (CIs) surrounding individual risk estimates. The median width of the individual 95%CrI was 5.3% (IQR 3.6-6.5) and 17% of the population had a 95%CrI width of 10% or greater. The uncertainty decreased with increasing sample size used for derivation of the model. Combining the Bayesian Weibull model with sampled hazard ratios based on trial reports may be used to estimate individual absolute risk reduction with uncertainty measures and the probability that a treatment option will result in a clinically relevant risk reduction. INTERPRETATION: Estimating uncertainty surrounding individual CVD risk predictions using Bayesian methods is feasible. The uncertainty regarding individual risk predictions could have several applications in clinical practice, like the comparison of different treatment options or by calculating the probability of the individual risk being below a certain treatment threshold. However, as the individual uncertainty measures only reflect sampling error and no biases in risk prediction, physicians should be familiar with the interpretation before widespread clinical adaption.

2.
Eur Heart J ; 2024 Jul 12.
Artículo en Inglés | MEDLINE | ID: mdl-38993086

RESUMEN

BACKGROUND AND AIMS: Home treatment is considered safe in acute pulmonary embolism (PE) patients selected by a validated triage tool (e.g. simplified PE severity index score or Hestia rule), but there is uncertainty regarding the applicability in underrepresented subgroups. The aim was to evaluate the safety of home treatment by performing an individual patient-level data meta-analysis. METHODS: Ten prospective cohort studies or randomized controlled trials were identified in a systematic search, totalling 2694 PE patients treated at home (discharged within 24 h) and identified by a predefined triage tool. The 14- and 30-day incidences of all-cause mortality and adverse events (combined endpoint of recurrent venous thromboembolism, major bleeding, and/or all-cause mortality) were evaluated. The relative risk (RR) for 14- and 30-day mortalities and adverse events is calculated in subgroups using a random effects model. RESULTS: The 14- and 30-day mortalities were 0.11% [95% confidence interval (CI) 0.0-0.24, I2 = 0) and 0.30% (95% CI 0.09-0.51, I2 = 0). The 14- and 30-day incidences of adverse events were 0.56% (95% CI 0.28-0.84, I2 = 0) and 1.2% (95% CI 0.79-1.6, I2 = 0). Cancer was associated with increased 30-day mortality [RR 4.9; 95% prediction interval (PI) 2.7-9.1; I2 = 0]. Pre-existing cardiopulmonary disease, abnormal troponin, and abnormal (N-terminal pro-)B-type natriuretic peptide [(NT-pro)BNP] at presentation were associated with an increased incidence of 14-day adverse events [RR 3.5 (95% PI 1.5-7.9, I2 = 0), 2.5 (95% PI 1.3-4.9, I2 = 0), and 3.9 (95% PI 1.6-9.8, I2 = 0), respectively], but not mortality. At 30 days, cancer, abnormal troponin, and abnormal (NT-pro)BNP were associated with an increased incidence of adverse events [RR 2.7 (95% PI 1.4-5.2, I2 = 0), 2.9 (95% PI 1.5-5.7, I2 = 0), and 3.3 (95% PI 1.6-7.1, I2 = 0), respectively]. CONCLUSIONS: The incidence of adverse events in home-treated PE patients, selected by a validated triage tool, was very low. Patients with cancer had a three- to five-fold higher incidence of adverse events and death. Patients with increased troponin or (NT-pro)BNP had a three-fold higher risk of adverse events, driven by recurrent venous thromboembolism and bleeding.

3.
JCO Clin Cancer Inform ; 8: e2400037, 2024 Jul.
Artículo en Inglés | MEDLINE | ID: mdl-39018510

RESUMEN

PURPOSE: Patient outcomes may differ from randomized trial averages. We aimed to predict benefit from FOLFOXIRI versus infusional fluorouracil, leucovorin, and oxaliplatin/fluorouracil, leucovorin, and irinotecan (FOLFOX/FOLFIRI), both plus bevacizumab, in patients with metastatic colorectal cancer (mCRC). METHODS: A Cox model with prespecified clinical, molecular, and laboratory variables was developed in 639 patients from the TRIBE2 trial for predicting 2-year mortality. Data from the CHARTA (n = 232), TRIBE1 (n = 504), and CAIRO5 (liver-only mCRC, n = 287) trials were used for external validation and heterogeneity of treatment effects (HTE) analysis. This involves categorizing patients into risk groups and assessing treatment effects across these groups. Performance was assessed by the C-index and calibration plots. The C-for-benefit was calculated to assess evidence for HTE. The c-for-benefit is specifically designed for HTE analysis. Like the commonly known c-statistic, it summarizes the discrimination of a model. Values over 0.5 indicate evidence for HTE. RESULTS: In TRIBE2, the overoptimism-corrected C-index was 0.66 (95% CI, 0.63 to 0.69). At external validation, the C-index was 0.69 (95% CI, 0.64 to 0.75), 0.68 (95% CI, 0.64 to 0.72), and 0.65 (95% CI, 0.65 to 0.66), in CHARTA, TRIBE1, and CAIRO5, respectively. Calibration plots indicated slight underestimation of mortality. The c-for-benefit indicated evidence for HTE in CHARTA (0.56, 95% CI, 0.48 to 0.65), but not in TRIBE1 (0.49, 95% CI, 0.44 to 0.55) and CAIRO5 (0.40, 95% CI, 0.32 to 0.48). CONCLUSION: Although 2-year mortality could be reasonably estimated, the HTE analysis showed that clinically available variables did not reliably identify which patients with mCRC benefit from FOLFOXIRI versus FOLFOX/FOLFIRI, both plus bevacizumab, across the three studies.


Asunto(s)
Protocolos de Quimioterapia Combinada Antineoplásica , Bevacizumab , Camptotecina , Neoplasias Colorrectales , Fluorouracilo , Leucovorina , Compuestos Organoplatinos , Humanos , Bevacizumab/administración & dosificación , Protocolos de Quimioterapia Combinada Antineoplásica/uso terapéutico , Protocolos de Quimioterapia Combinada Antineoplásica/efectos adversos , Neoplasias Colorrectales/tratamiento farmacológico , Neoplasias Colorrectales/patología , Neoplasias Colorrectales/mortalidad , Leucovorina/administración & dosificación , Leucovorina/uso terapéutico , Fluorouracilo/administración & dosificación , Masculino , Femenino , Camptotecina/análogos & derivados , Camptotecina/administración & dosificación , Camptotecina/uso terapéutico , Compuestos Organoplatinos/administración & dosificación , Compuestos Organoplatinos/uso terapéutico , Persona de Mediana Edad , Anciano , Metástasis de la Neoplasia , Resultado del Tratamiento , Pronóstico , Modelos de Riesgos Proporcionales , Adulto
5.
J Biomed Inform ; 155: 104666, 2024 Jul.
Artículo en Inglés | MEDLINE | ID: mdl-38848886

RESUMEN

OBJECTIVE: Class imbalance is sometimes considered a problem when developing clinical prediction models and assessing their performance. To address it, correction strategies involving manipulations of the training dataset, such as random undersampling or oversampling, are frequently used. The aim of this article is to illustrate the consequences of these class imbalance correction strategies on clinical prediction models' internal validity in terms of calibration and discrimination performances. METHODS: We used both heuristic intuition and formal mathematical reasoning to characterize the relations between conditional probabilities of interest and probabilities targeted when using random undersampling or oversampling. We propose a plug-in estimator that represents a natural correction for predictions obtained from models that have been trained on artificially balanced datasets ("naïve" models). We conducted a Monte Carlo simulation with two different data generation processes and present a real-world example using data from the International Stroke Trial database to empirically demonstrate the consequences of applying random resampling techniques for class imbalance correction on calibration and discrimination (in terms of Area Under the ROC, AUC) for logistic regression and tree-based prediction models. RESULTS: Across our simulations and in the real-world example, calibration of the naïve models was very poor. The models using the plug-in estimator generally outperformed the models relying on class imbalance correction in terms of calibration while achieving the same discrimination performance. CONCLUSION: Random resampling techniques for class imbalance correction do not generally improve discrimination performance (i.e., AUC), and their use is hard to justify when aiming at providing calibrated predictions. Improper use of such class imbalance correction techniques can lead to suboptimal data usage and less valid risk prediction models.


Asunto(s)
Método de Montecarlo , Humanos , Calibración , Curva ROC , Modelos Estadísticos , Área Bajo la Curva , Simulación por Computador , Modelos Logísticos , Algoritmos , Medición de Riesgo/métodos
6.
J Clin Epidemiol ; 172: 111387, 2024 May 08.
Artículo en Inglés | MEDLINE | ID: mdl-38729274

RESUMEN

Clinical prediction models provide risks of health outcomes that can inform patients and support medical decisions. However, most models never make it to actual implementation in practice. A commonly heard reason for this lack of implementation is that prediction models are often not externally validated. While we generally encourage external validation, we argue that an external validation is often neither sufficient nor required as an essential step before implementation. As such, any available external validation should not be perceived as a license for model implementation. We clarify this argument by discussing 3 common misconceptions about external validation. We argue that there is not one type of recommended validation design, not always a necessity for external validation, and sometimes a need for multiple external validations. The insights from this paper can help readers to consider, design, interpret, and appreciate external validation studies.

7.
Diabetes Res Clin Pract ; 212: 111684, 2024 Jun.
Artículo en Inglés | MEDLINE | ID: mdl-38697299

RESUMEN

AIMS: We investigated the differences in prevalence of acute coronary syndrome (ACS) by presence versus absence of diabetes in males and females with chest discomfort who called out-of-hours primary care (OHS-PC). METHODS: A cross-sectional study performed in the Netherlands. Patients who called the OHS-PC in the Utrecht region, the Netherlands between 2014 and 2017 with acute chest discomfort were included. We compared those with diabetes with those without diabetes. Multivariable logistic regression was used to determine the relation between diabetes and (i) high urgency allocation and (ii) ACS. RESULTS: Of the 2,195 callers with acute chest discomfort, 180 (8.2%) reported having diabetes. ACS was present in 15.3% of males (22.0% in those with diabetes) and 8.4% of females (18.8% in those with diabetes). Callers with diabetes did not receive a high urgency more frequently (74.4% vs. 67.8% (OR: 1.38; 95% CI 0.98-1.96). However, such callers had a higher odds for ACS (OR: 2.17; 95% CI 1.47-3.19). These differences were similar for females and males. CONCLUSIONS: Diabetes holds promise as diagnostic factor in callers to OHS-PC with chest discomfort. It might help triage in this setting given the increased risk of ACS in those with diabetes.


Asunto(s)
Síndrome Coronario Agudo , Atención Posterior , Dolor en el Pecho , Atención Primaria de Salud , Humanos , Masculino , Femenino , Estudios Transversales , Persona de Mediana Edad , Síndrome Coronario Agudo/epidemiología , Atención Primaria de Salud/estadística & datos numéricos , Atención Posterior/estadística & datos numéricos , Anciano , Dolor en el Pecho/epidemiología , Dolor en el Pecho/etiología , Países Bajos/epidemiología , Diabetes Mellitus/epidemiología , Prevalencia , Factores de Riesgo , Adulto
8.
Am J Epidemiol ; 2024 May 17.
Artículo en Inglés | MEDLINE | ID: mdl-38754869

RESUMEN

We spend a great deal of time on confounding in our teaching, in our methods development and in our assessment of study results. This may give the impression that uncontrolled confounding is the biggest problem that observational epidemiology faces, when in fact, other sources of bias such as selection bias, measurement error, missing data, and misalignment of zero time may often (especially if they are all present in a single study) lead to a stronger deviation from the truth. Compared to the amount of time we spend teaching how to address confounding in a data analysis, we spend relatively little time teaching methods for simulating confounding (and other sources of bias) to learn their impact and develop plans to mitigate or quantify the bias. We review a paper by Desai et al that uses simulation methods to quantify the impact of an unmeasured confounder when it is completely missing or when a proxy of the confounder is measured. We use this article to discuss how we can use simulations of sources of bias to ensure we generate better and more valid study estimates, and we discuss the importance of simulating realistic datasets with plausible bias structures to guide data collection. If an advanced life form exists outside of our current universe and they came to earth with the goal of scouring the published epidemiologic literature to understand what the biggest problem epidemiologists have, they would quickly discover that the limitations section of publications would provide them with all the information they needed. And most likely what they would conclude is that the biggest problem that we face is uncontrolled confounding. It seems to be an obsession of ours.

10.
J Clin Epidemiol ; 170: 111364, 2024 Jun.
Artículo en Inglés | MEDLINE | ID: mdl-38631529

RESUMEN

OBJECTIVES: To develop a framework to identify and evaluate spin practices and its facilitators in studies on clinical prediction model regardless of the modeling technique. STUDY DESIGN AND SETTING: We followed a three-phase consensus process: (1) premeeting literature review to generate items to be included; (2) a series of structured meetings to provide comments discussed and exchanged viewpoints on items to be included with a panel of experienced researchers; and (3) postmeeting review on final list of items and examples to be included. Through this iterative consensus process, a framework was derived after all panel's researchers agreed. RESULTS: This consensus process involved a panel of eight researchers and resulted in SPIN-Prediction Models which consists of two categories of spin (misleading interpretation and misleading transportability), and within these categories, two forms of spin (spin practices and facilitators of spin). We provide criteria and examples. CONCLUSION: We proposed this guidance aiming to facilitate not only the accurate reporting but also an accurate interpretation and extrapolation of clinical prediction models which will likely improve the reporting quality of subsequent research, as well as reduce research waste.


Asunto(s)
Consenso , Humanos , Proyectos de Investigación/normas , Modelos Estadísticos
12.
BMJ Open ; 14(4): e074818, 2024 Apr 16.
Artículo en Inglés | MEDLINE | ID: mdl-38626964

RESUMEN

OBJECTIVE: A subset of patients with superficial venous thrombosis (SVT) experiences clot propagation towards deep venous thrombosis (DVT) and/or pulmonary embolism (PE). The aim of this systematic review is to identify all clinically relevant cross-sectional and prognostic factors for predicting thrombotic complications in patients with SVT. DESIGN: Systematic review. DATA SOURCES: PubMed/MEDLINE and Embase were systematically searched until 3 March 2023. ELIGIBILITY CRITERIA: Original research studies with patients with SVT, DVT and/or PE as the outcome and presenting cross-sectional or prognostic predictive factors. DATA EXTRACTION AND SYNTHESIS OF RESULTS: The CHecklist for critical Appraisal and data extraction for systematic Reviews of prediction Modelling (CHARMS) checklist for prognostic factor studies was used for systematic extraction of study characteristics. Per identified predictive factor, relevant estimates of univariable and multivariable predictor-outcome associations were extracted, such as ORs and HRs. Estimates of association for the most frequently reported predictors were summarised in forest plots, and meta-analyses with heterogeneity were presented. The Quality in Prognosis Studies (QUIPS) tool was used for risk of bias assessment and Grading of Recommendations, Assessment, Development and Evaluations (GRADE) for assessing the certainty of evidence. RESULTS: Twenty-two studies were included (n=10 111 patients). The most reported predictive factors were high age, male sex, history of venous thromboembolism (VTE), absence of varicose veins and cancer. Pooled effect estimates were heterogenous and ranged from OR 3.12 (95% CI 1.75 to 5.59) for the cross-sectional predictor cancer to OR 0.92 (95% CI 0.56 to 1.53) for the prognostic predictor high age. The level of evidence was rated very low to low. Most studies were scored high or moderate risk of bias. CONCLUSIONS: Although the pooled estimates of the predictors high age, male sex, history of VTE, cancer and absence of varicose veins showed predictive potential in isolation, variability in study designs, lack of multivariable adjustment and high risk of bias prevent firm conclusions. High-quality, multivariable studies are necessary to be able to identify individual SVT risk profiles. PROSPERO REGISTRATION NUMBER: CRD42021262819.


Asunto(s)
Neoplasias , Embolia Pulmonar , Várices , Tromboembolia Venosa , Trombosis de la Vena , Humanos , Masculino , Tromboembolia Venosa/prevención & control , Estudios Transversales , Factores de Riesgo , Trombosis de la Vena/complicaciones , Embolia Pulmonar/etiología , Neoplasias/complicaciones , Anticoagulantes/uso terapéutico
13.
BMJ Open ; 14(3): e075475, 2024 Mar 23.
Artículo en Inglés | MEDLINE | ID: mdl-38521534

RESUMEN

OBJECTIVE: To identify and synthesise relevant existing prognostic factors (PF) and prediction models (PM) for hospitalisation and all-cause mortality within 90 days in primary care patients with acute lower respiratory tract infections (LRTI). DESIGN: Systematic review. METHODS: Systematic searches of MEDLINE, Embase and the Cochrane Library were performed. All PF and PM studies on the risk of hospitalisation or all-cause mortality within 90 days in adult primary care LRTI patients were included. The risk of bias was assessed using the Quality in Prognostic Studies tool and Prediction Model Risk Of Bias Assessment Tool tools for PF and PM studies, respectively. The results of included PF and PM studies were descriptively summarised. RESULTS: Of 2799 unique records identified, 16 were included: 9 PF studies, 6 PM studies and 1 combination of both. The risk of bias was judged high for all studies, mainly due to limitations in the analysis domain. Based on reported multivariable associations in PF studies, increasing age, sex, current smoking, diabetes, a history of stroke, cancer or heart failure, previous hospitalisation, influenza vaccination (negative association), current use of systemic corticosteroids, recent antibiotic use, respiratory rate ≥25/min and diagnosis of pneumonia were identified as most promising candidate predictors. One newly developed PM was externally validated (c statistic 0.74, 95% CI 0.71 to 0.78) whereas the previously hospital-derived CRB-65 was externally validated in primary care in five studies (c statistic ranging from 0.72 (95% CI 0.63 to 0.81) to 0.79 (95% CI 0.65 to 0.92)). None of the PM studies reported measures of model calibration. CONCLUSIONS: Implementation of existing models for individualised risk prediction of 90-day hospitalisation or mortality in primary care LRTI patients in everyday practice is hampered by incomplete assessment of model performance. The identified candidate predictors provide useful information for clinicians and warrant consideration when developing or updating PMs using state-of-the-art development and validation techniques. PROSPERO REGISTRATION NUMBER: CRD42022341233.


Asunto(s)
Infecciones del Sistema Respiratorio , Adulto , Humanos , Pronóstico , Infecciones del Sistema Respiratorio/tratamiento farmacológico , Antibacterianos/uso terapéutico , Hospitalización , Atención Primaria de Salud
14.
BMC Prim Care ; 25(1): 101, 2024 Mar 27.
Artículo en Inglés | MEDLINE | ID: mdl-38539092

RESUMEN

BACKGROUND: In out-of-hours primary care (OHS-PC), semi-automatic decision support tools are often used during telephone triage. In the Netherlands, the Netherlands Triage Standard (NTS) is used. The NTS is mainly expert-based and evidence on the diagnostic accuracy of the NTS' urgency allocation against clinically relevant outcomes for patients calling with shortness of breath (SOB) is lacking. METHODS: We included data from adults (≥18 years) who contacted two large Dutch OHS-PC centres for SOB between 1 September 2020 and 31 August 2021 and whose follow-up data about final diagnosis could be retrieved from their own general practitioner (GP). The diagnostic accuracy (sensitivity and specificity with corresponding 95% confidence intervals (CI)) of the NTS' urgency levels (high (U1/U2) versus low (U3/U4/U5) and 'final' urgency levels (including overruling of the urgency by triage nurses or supervising general practitioners (GPs)) was determined with life-threatening events (LTEs) as the reference. LTEs included, amongst others, acute coronary syndrome, pulmonary embolism, acute heart failure and severe pneumonia. RESULTS: Out of 2012 eligible triage calls, we could include 1833 adults with SOB who called the OHS-PC, mean age 53.3 (SD 21.5) years, 55.5% female, and 16.6% showed to have had a LTE. Most often severe COVID-19 infection (6.0%), acute heart failure (2.6%), severe COPD exacerbation (2.1%) or severe pneumonia (1.9%). The NTS urgency level had a sensitivity of 0.56 (95% CI 0.50-0.61) and specificity of 0.61 (95% CI 0.58-0.63). Overruling of the NTS' urgency allocation by triage nurses and/or supervising GPs did not impact sensitivity (0.56 vs. 0.54, p = 0.458) but slightly improved specificity (0.61 vs. 0.65, p < 0.001). CONCLUSIONS: The semi-automatic decision support tool NTS performs poorly with respect to safety (sensitivity) and efficiency (specificity) of urgency allocation in adults calling Dutch OHS-PC with SOB. There is room for improvement of telephone triage in patients calling OHS-PC with SOB. TRIAL REGISTRATION: The Netherlands Trial Register, number: NL9682 .


Asunto(s)
Atención Posterior , Insuficiencia Cardíaca , Neumonía , Adulto , Humanos , Femenino , Persona de Mediana Edad , Masculino , Estudios Transversales , Disnea/diagnóstico , Atención Posterior/métodos , Atención Primaria de Salud/métodos
15.
J Clin Epidemiol ; 168: 111270, 2024 Apr.
Artículo en Inglés | MEDLINE | ID: mdl-38311188

RESUMEN

OBJECTIVES: To systematically evaluate the performance of COVID-19 prognostic models and scores for mortality risk in older populations across three health-care settings: hospitals, primary care, and nursing homes. STUDY DESIGN AND SETTING: This retrospective external validation study included 14,092 older individuals of ≥70 years of age with a clinical or polymerase chain reaction-confirmed COVID-19 diagnosis from March 2020 to December 2020. The six validation cohorts include three hospital-based (CliniCo, COVID-OLD, COVID-PREDICT), two primary care-based (Julius General Practitioners Network/Academisch network huisartsgeneeskunde/Network of Academic general Practitioners, PHARMO), and one nursing home cohort (YSIS) in the Netherlands. Based on a living systematic review of COVID-19 prediction models using Prediction model Risk Of Bias ASsessment Tool for quality and risk of bias assessment and considering predictor availability in validation cohorts, we selected six prognostic models predicting mortality risk in adults with COVID-19 infection (GAL-COVID-19 mortality, 4C Mortality Score, National Early Warning Score 2-extended model, Xie model, Wang clinical model, and CURB65 score). All six prognostic models were validated in the hospital cohorts and the GAL-COVID-19 mortality model was validated in all three healthcare settings. The primary outcome was in-hospital mortality for hospitals and 28-day mortality for primary care and nursing home settings. Model performance was evaluated in each validation cohort separately in terms of discrimination, calibration, and decision curves. An intercept update was performed in models indicating miscalibration followed by predictive performance re-evaluation. MAIN OUTCOME MEASURE: In-hospital mortality for hospitals and 28-day mortality for primary care and nursing home setting. RESULTS: All six prognostic models performed poorly and showed miscalibration in the older population cohorts. In the hospital settings, model performance ranged from calibration-in-the-large -1.45 to 7.46, calibration slopes 0.24-0.81, and C-statistic 0.55-0.71 with 4C Mortality Score performing as the most discriminative and well-calibrated model. Performance across health-care settings was similar for the GAL-COVID-19 model, with a calibration-in-the-large in the range of -2.35 to -0.15 indicating overestimation, calibration slopes of 0.24-0.81 indicating signs of overfitting, and C-statistic of 0.55-0.71. CONCLUSION: Our results show that most prognostic models for predicting mortality risk performed poorly in the older population with COVID-19, in each health-care setting: hospital, primary care, and nursing home settings. Insights into factors influencing predictive model performance in the older population are needed for pandemic preparedness and reliable prognostication of health-related outcomes in this demographic.


Asunto(s)
COVID-19 , Adulto , Humanos , Anciano , Pronóstico , COVID-19/diagnóstico , Estudios Retrospectivos , Prueba de COVID-19 , Casas de Salud , Hospitales , Mortalidad Hospitalaria , Atención Primaria de Salud
16.
BMC Prim Care ; 25(1): 70, 2024 02 23.
Artículo en Inglés | MEDLINE | ID: mdl-38395766

RESUMEN

BACKGROUND: During the COVID-19 pandemic, older patients in primary care were triaged based on their frailty or assumed vulnerability for poor outcomes, while evidence on the prognostic value of vulnerability measures in COVID-19 patients in primary care was lacking. Still, knowledge on the role of vulnerability is pivotal in understanding the resilience of older people during acute illness, and hence important for future pandemic preparedness. Therefore, we assessed the predictive value of different routine care-based vulnerability measures in addition to age and sex for 28-day mortality in an older primary care population of patients with COVID-19. METHODS: From primary care medical records using three routinely collected Dutch primary care databases, we included all patients aged 70 years or older with a COVID-19 diagnosis registration in 2020 and 2021. All-cause mortality was predicted using logistic regression based on age and sex only (basic model), and separately adding six vulnerability measures: renal function, cognitive impairment, number of chronic drugs, Charlson Comorbidity Index, Chronic Comorbidity Score, and a Frailty Index. Predictive performance of the basic model and the six vulnerability models was compared in terms of area under the receiver operator characteristic curve (AUC), index of prediction accuracy and the distribution of predicted risks. RESULTS: Of the 4,065 included patients, 9% died within 28 days after COVID-19 diagnosis. Predicted mortality risk ranged between 7-26% for the basic model including age and sex, changing to 4-41% by addition of comorbidity-based vulnerability measures (Charlson Comorbidity Index, Chronic Comorbidity Score), more reflecting impaired organ functioning. Similarly, the AUC of the basic model slightly increased from 0.69 (95%CI 0.66 - 0.72) to 0.74 (95%CI 0.71 - 0.76) by addition of either of these comorbidity scores. Addition of a Frailty Index, renal function, the number of chronic drugs or cognitive impairment yielded no substantial change in predictions. CONCLUSION: In our dataset of older COVID-19 patients in primary care, the 28-day mortality fraction was substantial at 9%. Six different vulnerability measures had little incremental predictive value in addition to age and sex in predicting short-term mortality.


Asunto(s)
COVID-19 , Fragilidad , Humanos , Anciano , Fragilidad/diagnóstico , Pandemias , Prueba de COVID-19 , Atención Primaria de Salud
18.
J Clin Epidemiol ; 167: 111258, 2024 Mar.
Artículo en Inglés | MEDLINE | ID: mdl-38219811

RESUMEN

OBJECTIVES: Natural language processing (NLP) of clinical notes in electronic medical records is increasingly used to extract otherwise sparsely available patient characteristics, to assess their association with relevant health outcomes. Manual data curation is resource intensive and NLP methods make these studies more feasible. However, the methodology of using NLP methods reliably in clinical research is understudied. The objective of this study is to investigate how NLP models could be used to extract study variables (specifically exposures) to reliably conduct exposure-outcome association studies. STUDY DESIGN AND SETTING: In a convenience sample of patients admitted to the intensive care unit of a US academic health system, multiple association studies are conducted, comparing the association estimates based on NLP-extracted vs. manually extracted exposure variables. The association studies varied in NLP model architecture (Bidirectional Encoder Decoder from Transformers, Long Short-Term Memory), training paradigm (training a new model, fine-tuning an existing external model), extracted exposures (employment status, living status, and substance use), health outcomes (having a do-not-resuscitate/intubate code, length of stay, and in-hospital mortality), missing data handling (multiple imputation vs. complete case analysis), and the application of measurement error correction (via regression calibration). RESULTS: The study was conducted on 1,174 participants (median [interquartile range] age, 61 [50, 73] years; 60.6% male). Additionally, up to 500 discharge reports of participants from the same health system and 2,528 reports of participants from an external health system were used to train the NLP models. Substantial differences were found between the associations based on NLP-extracted and manually extracted exposures under all settings. The error in association was only weakly correlated with the overall F1 score of the NLP models. CONCLUSION: Associations estimated using NLP-extracted exposures should be interpreted with caution. Further research is needed to set conditions for reliable use of NLP in medical association studies.


Asunto(s)
Unidades de Cuidados Intensivos , Procesamiento de Lenguaje Natural , Humanos , Masculino , Persona de Mediana Edad , Femenino , Registros Electrónicos de Salud
19.
Stat Med ; 43(6): 1119-1134, 2024 Mar 15.
Artículo en Inglés | MEDLINE | ID: mdl-38189632

RESUMEN

Tuning hyperparameters, such as the regularization parameter in Ridge or Lasso regression, is often aimed at improving the predictive performance of risk prediction models. In this study, various hyperparameter tuning procedures for clinical prediction models were systematically compared and evaluated in low-dimensional data. The focus was on out-of-sample predictive performance (discrimination, calibration, and overall prediction error) of risk prediction models developed using Ridge, Lasso, Elastic Net, or Random Forest. The influence of sample size, number of predictors and events fraction on performance of the hyperparameter tuning procedures was studied using extensive simulations. The results indicate important differences between tuning procedures in calibration performance, while generally showing similar discriminative performance. The one-standard-error rule for tuning applied to cross-validation (1SE CV) often resulted in severe miscalibration. Standard non-repeated and repeated cross-validation (both 5-fold and 10-fold) performed similarly well and outperformed the other tuning procedures. Bootstrap showed a slight tendency to more severe miscalibration than standard cross-validation-based tuning procedures. Differences between tuning procedures were larger for smaller sample sizes, lower events fractions and fewer predictors. These results imply that the choice of tuning procedure can have a profound influence on the predictive performance of prediction models. The results support the application of standard 5-fold or 10-fold cross-validation that minimizes out-of-sample prediction error. Despite an increased computational burden, we found no clear benefit of repeated over non-repeated cross-validation for hyperparameter tuning. We warn against the potentially detrimental effects on model calibration of the popular 1SE CV rule for tuning prediction models in low-dimensional settings.


Asunto(s)
Proyectos de Investigación , Humanos , Simulación por Computador , Tamaño de la Muestra
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