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BACKGROUND: Patients with chronic lung diseases (CLDs), defined as progressive and life-limiting respiratory conditions, experience a heavy symptom burden as the conditions become more advanced, but palliative referral rates are low and late. Prognostic tools can help clinicians identify CLD patients at high risk of deterioration for needs assessments and referral to palliative care. As current prognostic tools may not generalize well across all CLD conditions, we aim to develop and validate a general model to predict one-year mortality in patients presenting with any CLD. METHODS: A retrospective cohort study of patients with a CLD diagnosis at a public hospital from July 2016 to October 2017 was conducted. The outcome of interest was all-cause mortality within one-year of diagnosis. Potential prognostic factors were identified from reviews of prognostic studies in CLD, and data was extracted from electronic medical records. Missing data was imputed using multiple imputation by chained equations. Logistic regression models were developed using variable selection methods and validated in patients seen from January 2018 to December 2019. Discriminative ability, calibration and clinical usefulness of the model was assessed. Model coefficients and performance were pooled across all imputed datasets and reported. RESULTS: Of the 1000 patients, 122 (12.2%) died within one year. Patients had chronic obstructive pulmonary disease or emphysema (55%), bronchiectasis (38%), interstitial lung diseases (12%), or multiple diagnoses (6%). The model selected through forward stepwise variable selection had the highest AUC (0.77 (0.72-0.82)) and consisted of ten prognostic factors. The model AUC for the validation cohort was 0.75 (0.70, 0.81), and the calibration intercept and slope were - 0.14 (-0.54, 0.26) and 0.74 (0.53, 0.95) respectively. Classifying patients with a predicted risk of death exceeding 0.30 as high risk, the model would correctly identify 3 out 10 decedents and 9 of 10 survivors. CONCLUSIONS: We developed and validated a prognostic model for one-year mortality in patients with CLD using routinely available administrative data. The model will support clinicians in identifying patients across various CLD etiologies who are at risk of deterioration for a basic palliative care assessment to identify unmet needs and trigger an early referral to palliative medicine. TRIAL REGISTRATION: Not applicable (retrospective study).
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Enfermedades Pulmonares , Humanos , Femenino , Masculino , Pronóstico , Estudios Retrospectivos , Anciano , Persona de Mediana Edad , Enfermedades Pulmonares/mortalidad , Enfermedades Pulmonares/diagnóstico , Enfermedad Crónica , Anciano de 80 o más Años , Enfermedad Pulmonar Obstructiva Crónica/mortalidad , Enfermedad Pulmonar Obstructiva Crónica/diagnóstico , Cuidados Paliativos , Modelos Logísticos , Enfermedades Pulmonares Intersticiales/mortalidad , Enfermedades Pulmonares Intersticiales/diagnósticoRESUMEN
Introduction: Identifying the evolving needs of patients with advanced heart failure (AdHF) and triaging those at high risk of death can facilitate timely referrals to palliative care and advance patient-centered individualized care. There are limited models specific for patients with end-stage HF. We aim to identify risk factors associated with up to three-year all-cause mortality (ACM) and describe prognostic models developed or validated in AdHF populations. Methods: Frameworks proposed by Arksey, O'Malley, and Levac were adopted for this scoping review. We searched the Medline, EMBASE, PubMed, CINAHL, Cochrane library, Web of Science and gray literature databases for articles published between January 2010 and September 2020. Primary studies that included adults aged ≥ 18 years, diagnosed with AdHF defined as New York Heart Association class III/IV, American Heart Association/American College of Cardiology Stage D, end-stage HF, and assessed for risk factors associated with up to three-year ACM using multivariate analysis were included. Studies were appraised using the Quality of Prognostic Studies tool. Data were analyzed using a narrative synthesis approach. Results: We reviewed 167 risk factors that were associated with up to three-year ACM and prognostic models specific to AdHF patients across 65 articles with low-to-moderate bias. Studies were mostly based in Western and/or European cohorts (n = 60), in the acute care setting (n = 56), and derived from clinical trials (n = 40). Risk factors were grouped into six domains. Variables related to cardiovascular and overall health were frequently assessed. Ten prognostic models developed/validated on AdHF patients displayed acceptable model performance [area under the curve (AUC) range: 0.71-0.81]. Among the ten models, the model for end-stage-liver disease (MELD-XI) and acute decompensated HF with N-terminal pro b-type natriuretic peptide (ADHF/proBNP) model attained the highest discriminatory performance against short-term ACM (AUC: 0.81). Conclusions: To enable timely referrals to palliative care interventions, further research is required to develop or validate prognostic models that consider the evolving landscape of AdHF management.
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BACKGROUND: Challenges in prognosticating patients diagnosed with advanced dementia (AD) hinders timely referrals to palliative care. We aim to develop and validate a prognostic model to predict one-year all-cause mortality (ACM) in patients with AD presenting at an acute care hospital. METHODS: This retrospective cohort study utilised administrative and clinical data from Tan Tock Seng Hospital (TTSH). Patients admitted to TTSH between 1st July 2016 and 31st October 2017 and identified to have AD were included. The primary outcome was ACM within one-year of AD diagnosis. Multivariable logistic regression was used. The PROgnostic Model for Advanced Dementia (PRO-MADE) was internally validated using a bootstrap resampling of 1000 replications and externally validated on a more recent cohort of AD patients. The model was evaluated for overall predictive accuracy (Nagelkerke's R2 and Brier score), discriminative [area-under-the-curve (AUC)], and calibration [calibration slope and calibration-in-the-large (CITL)] properties. RESULTS: A total of 1,077 patients with a mean age of 85 (SD: 7.7) years old were included, and 318 (29.5%) patients died within one-year of AD diagnosis. Predictors of one-year ACM were age > 85 years (OR:1.87; 95%CI:1.36 to 2.56), male gender (OR:1.62; 95%CI:1.18 to 2.22), presence of pneumonia (OR:1.75; 95%CI:1.25 to 2.45), pressure ulcers (OR:2.60; 95%CI:1.57 to 4.31), dysphagia (OR:1.53; 95%CI:1.11 to 2.11), Charlson Comorbidity Index ≥ 8 (OR:1.39; 95%CI:1.01 to 1.90), functional dependency in ≥ 4 activities of daily living (OR: 1.82; 95%CI:1.32 to 2.53), abnormal urea (OR:2.16; 95%CI:1.58 to 2.95) and abnormal albumin (OR:3.68; 95%CI:2.07 to 6.54) values. Internal validation results for optimism-adjusted Nagelkerke's R2, Brier score, AUC, calibration slope and CITL were 0.25 (95%CI:0.25 to 0.26), 0.17 (95%CI:0.17 to 0.17), 0.76 (95%CI:0.76 to 0.76), 0.95 (95% CI:0.95 to 0.96) and 0 (95%CI:-0.0001 to 0.001) respectively. When externally validated, the model demonstrated an AUC of 0.70 (95%CI:0.69 to 0.71), calibration slope of 0.64 (95%CI:0.63 to 0.66) and CITL of -0.27 (95%CI:-0.28 to -0.26). CONCLUSION: The PRO-MADE attained good discrimination and calibration properties. Used synergistically with a clinician's judgement, this model can identify AD patients who are at high-risk of one-year ACM to facilitate timely referrals to palliative care.