Identifying High-Cost, High-Risk Patients Using Administrative Databases in Tuscany, Italy.
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ABSTRACT:
SUBMITTER: Bellini I
PROVIDER: S-EPMC5523251 | biostudies-other | 2017
REPOSITORIES: biostudies-other
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Project description:ObjectivesCase ascertainment algorithms were developed and validated to identify people living with cirrhosis in administrative health data in Manitoba, Canada using primary care electronic medical records (EMR) to define the reference standards.MethodsWe linked provincial administrative health data to primary care EMR data. The validation cohort included 116,675 Manitobans aged >18 years with at least one primary care visit between April 1998 and March 2015. Hospital records, physician billing claims, vital statistics, and prescription drug data were used to develop and test 93 case-finding algorithms. A validated case definition for primary care EMR data was the reference standard. We estimated sensitivity, specificity, positive and negative predictive values (PPV, NPV), Youden's index, area under the receiver operative curve, and their 95% confidence intervals (CIs).ResultsA total of 116,675 people were in the validation cohort. The prevalence of cirrhosis was 1.4% (n = 1593). Algorithm sensitivity estimates ranged from 32.5% (95% CI 32.2-32.8) to 68.3% (95% CI 68.0-68.9) and PPV from 17.4% (95% CI 17.1-17.6) to 23.4% (95% CI 23.1-23.6). Specificity (95.5-98.2) and NPV (approximately 99%) were high for all algorithms. The algorithms had slightly higher sensitivity estimates among men compared with women, and individuals aged ≥45 years compared to those aged 18-44 years.ConclusionCirrhosis algorithms applied to administrative health data had moderate validity when a validated case definition for primary care EMRs was the reference standard. This study provides algorithms for identifying diagnosed cirrhosis cases for population-based research and surveillance studies.
| S-EPMC10946181 | biostudies-literature
Project description:Peripheral artery disease (PAD) carries a high burden of morbidity when identified in patients with coronary artery disease (CAD). However, identification of patients with concomitant CAD and PAD remains challenging. Using linked administrative databases of 207,026 individuals with CAD between 2002 and 2019 (median follow-up, 4.7 years), a model for PAD was applied to identify baseline PAD and the development of PAD during follow-up. Both baseline PAD and future PAD models demonstrated poor calibration and discrimination (c-statistic 0.618 and 0.583). In the absence of additional variables, the present models are unable to identify patients with concomitant CAD and PAD.
| S-EPMC10516714 | biostudies-literature
Project description:Bossoleto mofette, Tuscany, Italy
| PRJNA548940 | ENA
Project description:A recent comprehensive report on healthcare quality in Italy published by the Organization of Economic Co-operation and Development (OECD) recommended that regular monitoring of quality of primary care by means of compliance with standards of care for chronic diseases is performed. A previous ecological study demonstrated that compliance with standards of care could be reliably estimated on regional level using administrative databases. This study compares estimates based on administrative data with estimates based on GP records for the same persons, to understand whether ecological fallacy played a role in the results of the previous study.We compared estimates of compliance with diagnostic and therapeutic standards of care for type 2 diabetes (T2DM), hypertension and ischaemic heart disease (IHD) from administrative data (IAD) with estimates from medical records (MR) for the same persons registered with 24 GP's in 2012. Data were linked at an individual level.32,688 persons entered the study, 12,673 having at least one of the three diseases according to at least one data source. Patients not detected by IAD were many, for all three conditions: adding MR increased the number of cases of T2DM, hypertension, and IHD by +40%, +42%, and +104%, respectively. IAD had imperfect sensitivity in detecting population compliance with therapies (adding MR increased the estimate, from +11.5% for statins to +14.7% for antithrombotics), and, more substantially, with diagnostic recommendations (adding MR increased the estimate, from +23.7% in glycated hemoglobin tests, to +50.5% in electrocardiogram). Patients not detected by IAD were less compliant with respect to those that IAD correctly identified (from -4.8 percentage points in proportion of IHD patients compliant with a yearly glycated hemoglobin test, to -40.1 points in the proportion of T2DM patients compliant with the same recommendation). IAD overestimated indicators of compliance with therapeutic standards (significant differences ranged from 3.3. to 3.6 percentage points) and underestimated indicators of compliance with diagnostic standards (significant differences ranged from -2.3 to -14.1 percentage points).IAD overestimated the percentage of patients compliant with therapeutic standards by less than 6 percentage points, and underestimated the percentage of patients compliant with diagnostic standards by a maximum of 14 percentage points. Therefore, both discussions at local level between GP's and local health unit managers and discussions at central level between national and regional policy makers can be informed by indicators of compliance estimated by IAD, which, based on those results, have the ability of signalling critical or excellent clusters. However, this study found that estimates are partly flawed, because a high number of patients with chronic diseases are not detected by IAD, patients detected are not representative of the whole population of patients, and some categories of diagnostic tests are markedly underrecorded in IAD (up to 50% in the case of electrocardiograms). Those results call to caution when interpreting IAD estimates. Audits based on medical records, on the local level, and an interpretation taking into account information external to IAD, on the central level, are needed to assess a more comprehensive compliance with standards.
| S-EPMC5726627 | biostudies-literature
Project description:ObjectiveTo develop algorithms to identify patients with advanced heart failure (HF) that can be applied to administrative data.Patients and methodsIn a population-based cohort of all residents of Olmsted County, Minnesota, with greater than or equal to 1 HF billing code 2007-2017 (n=8657), we identified all patients with advanced HF (n=847) by applying the gold standard European Society of Cardiology advanced HF criteria via manual medical review by an HF cardiologist. The advanced HF index date was the date the patient first met all European Society of Cardiology criteria. We subsequently developed candidate algorithms to identify advanced HF using administrative data (billing codes and prescriptions relevant to HF or comorbidities that affect HF outcomes), applied them to the HF cohort, and assessed their ability to identify patients with advanced HF on or after their advanced HF index date.ResultsA single hospitalization for HF or ventricular arrhythmias identified all patients with advanced HF (sensitivity, 100%); however, the positive predictive value (PPV) was low (36.4%). More stringent definitions, including additional hospitalizations and/or other signs of advanced HF (hyponatremia, acute kidney injury, hypotension, or high-dose diuretic use), decreased the sensitivity but improved the specificity and PPV. For example, 2 hospitalizations plus 1 sign of advanced HF had a sensitivity of 72.7%, specificity of 89.8%, and PPV of 60.5%. Negative predictive values were high for all algorithms evaluated.ConclusionAlgorithms using administrative data can identify patients with advanced HF with reasonable performance.
| S-EPMC8968660 | biostudies-literature
Project description:Retrospective administrative claims database studies provide real-world evidence about treatment patterns, healthcare resource use, and costs for patients and are increasingly used to inform policy-making, drug formulary, and regulatory decisions. However, there is no standard methodology to identify patients with pulmonary arterial hypertension (PAH) from administrative claims data. Given the number of approved drugs now available for patients with PAH, the cost of PAH treatments, and the significant healthcare resource use associated with the care of patients with PAH, there is a considerable need to develop an evidence-based and systematic approach to accurately identify these patients in claims databases. A panel of pulmonary hypertension clinical experts and researchers experienced in retrospective claims database studies convened to review relevant literature and recommend best practices for developing algorithms to identify patients with PAH in administrative claims databases specific to a particular research hypothesis.
| S-EPMC6600840 | biostudies-literature
Project description:BackgroundHealth administrative databases are essential to define patient populations, make socioeconomic predictions, and facilitate medical research and healthcare planning. The accuracy of this data is dependent on valid codes/coding algorithms.AimsThe aim of this study was to systematically identify and summarize the validity of International Classification of Diseases (ICD) codes for identifying patients with cirrhosis in administrative data.MethodsElectronic databases, MEDLINE (via Ovid), EMBASE (via Ovid), the Web of Science, and CINAHL (via EBSCOhost), were searched for validation studies which compared ICD codes related to cirrhosis to a clinical reference standard, and reported statistical measures of performance.ResultsFourteen studies were included in the review. There was a large variation in the algorithms used to validate ICD codes to diagnose cirrhosis. Despite the variation, the positive predictive value (PPV) was greater than 84% and the specificity was greater than 75% in the majority of the studies. The negative predictive value (NPV) was lower, but still was associated with values greater than 70% in the majority of studies. Sensitivity data varied significantly with values ranging from 0.27 to 99%.ConclusionsEvaluated ICD codes for cirrhosis, including codes for chronic liver disease, cirrhosis-specific codes, and cirrhosis-related complications, have demonstrated variable sensitivity and reasonable specificity for the identification of cirrhosis. Additional research is needed to maximize the identification of persons with cirrhosis to avoid underestimating the burden of disease.
| S-EPMC10291482 | biostudies-literature
Project description:ObjectiveMany healthcare systems employ population-based risk scores to prospectively identify patients at high risk of poor outcomes, but it is unclear whether single point-in-time scores adequately represent future risk. We sought to identify and characterize latent subgroups of high-risk patients based on risk score trajectories.Study designObservational study of 7289 patients discharged from Veterans Health Administration (VA) hospitals during a 1-week period in November 2012 and categorized in the top 5th percentile of risk for hospitalization.MethodsUsing VA administrative data, we calculated weekly risk scores using the validated Care Assessment Needs model, reflecting the predicted probability of hospitalization. We applied the non-parametric k-means algorithm to identify latent subgroups of patients based on the trajectory of patients' hospitalization probability over a 2-year period. We then compared baseline sociodemographic characteristics, comorbidities, health service use, and social instability markers between identified latent subgroups.ResultsThe best-fitting model identified two subgroups: moderately high and persistently high risk. The moderately high subgroup included 65% of patients and was characterized by moderate subgroup-level hospitalization probability decreasing from 0.22 to 0.10 between weeks 1 and 66, then remaining constant through the study end. The persistently high subgroup, comprising the remaining 35% of patients, had a subgroup-level probability increasing from 0.38 to 0.41 between weeks 1 and 52, and declining to 0.30 at study end. Persistently high-risk patients were older, had higher prevalence of social instability and comorbidities, and used more health services.ConclusionsOn average, one third of patients initially identified as high risk stayed at very high risk over a 2-year follow-up period, while risk for the other two thirds decreased to a moderately high level. This suggests that multiple approaches may be needed to address high-risk patient needs longitudinally or intermittently.
| S-EPMC6258600 | biostudies-literature
Project description:BackgroundMachine learning-based risk prediction models may outperform traditional statistical models in large datasets with many variables, by identifying both novel predictors and the complex interactions between them. This study compared deep learning extensions of survival analysis models with Cox proportional hazards models for predicting cardiovascular disease (CVD) risk in national health administrative datasets.MethodsUsing individual person linkage of administrative datasets, we constructed a cohort of all New Zealanders aged 30-74 who interacted with public health services during 2012. After excluding people with prior CVD, we developed sex-specific deep learning and Cox proportional hazards models to estimate the risk of CVD events within 5 years. Models were compared based on the proportion of explained variance, model calibration and discrimination, and hazard ratios for predictor variables.ResultsFirst CVD events occurred in 61 927 of 2 164 872 people. Within the reference group, the largest hazard ratios estimated by the deep learning models were for tobacco use in women (2.04, 95% CI: 1.99, 2.10) and chronic obstructive pulmonary disease with acute lower respiratory infection in men (1.56, 95% CI: 1.50, 1.62). Other identified predictors (e.g. hypertension, chest pain, diabetes) aligned with current knowledge about CVD risk factors. Deep learning outperformed Cox proportional hazards models on the basis of proportion of explained variance (R2: 0.468 vs 0.425 in women and 0.383 vs 0.348 in men), calibration and discrimination (all P <0.0001).ConclusionsDeep learning extensions of survival analysis models can be applied to large health administrative datasets to derive interpretable CVD risk prediction equations that are more accurate than traditional Cox proportional hazards models.
| S-EPMC9189958 | biostudies-literature
Project description:Objective: To assess the validity of a treatments- and tests-based Case-Finding Algorithm for identifying patients with non-small cell lung cancer (NSCLC) from claims databases. Data sources: Primary data from the HealthCore Integrated Research Environment (HIRE)-Oncology database and the HealthCore Integrated Research Database (HIRD) were collected between June 1, 2014, and October 31, 2015. Study design: A comparative statistical evaluation using receiver operating characteristic (ROC) curve analysis and other validity measures was used to validate the NSCLC Case-Finding Algorithm vs. a control algorithm. Data collection: Patients with lung cancer were identified based on diagnosis and pathology classifications as NSCLC or small-cell lung cancer. Records from identified patients were linked to claims data from Anthem health plans. Three-month pre-index and post-index data were included. Principal findings: The NSCLC Case-Finding Algorithm had an area under the curve (AUC) of 0.88 compared with 0.53 in the control (p < 0.0001). Promising diagnostic accuracy was observed for the NSCLC Case-Finding Algorithm based on sensitivity (94.8%), specificity (81.1%), positive predictive value (PPV) (95.3%), negative predictive value (NPV) (79.6%), accuracy (92.1%), and diagnostic odds ratio (DOR) (78.8). Conclusions: The NSCLC Case-Finding Algorithm demonstrated strong validity for distinguishing patients with NSCLC from those with SCLC in claims data records and can be used for research into NSCLC populations.
| S-EPMC5714924 | biostudies-literature